diff --git a/docs/turbine_interaction.ipynb b/docs/turbine_interaction.ipynb
index 13c5e9d97..bf02cb008 100644
--- a/docs/turbine_interaction.ipynb
+++ b/docs/turbine_interaction.ipynb
@@ -251,10 +251,10 @@
      "output_type": "stream",
      "text": [
       "iea_15MW_floating\n",
-      "iea_10MW\n",
-      "iea_15MW\n",
       "iea_15MW_multi_dim_cp_ct\n",
-      "nrel_5MW\n"
+      "iea_15MW\n",
+      "nrel_5MW\n",
+      "iea_10MW\n"
      ]
     }
    ],
@@ -264,9 +264,9 @@
     "\n",
     "# Load the internal library, except the 20 MW turbine\n",
     "tl.load_internal_library(exclude=[\n",
-    "    \"iea_10MW.yaml\",\n",
-    "    \"iea_15MW.yaml\",\n",
-    "    \"nrel_5MW.yaml\",\n",
+    "    \"iea_10MW_v3legacy.yaml\",\n",
+    "    \"iea_15MW_floating_multi_dim_cp_ct_v3legacy.yaml\",\n",
+    "    \"iea_15MW_v3legacy.yaml\",\n",
     "    \"nrel_5MW_v3legacy.yaml\",\n",
     "    \"x_20MW.yaml\",\n",
     "])\n",
@@ -296,24 +296,13 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iea_15MW_floating\n",
-      "iea_10MW\n",
-      "iea_15MW\n",
-      "iea_15MW_multi_dim_cp_ct\n",
-      "nrel_5MW\n",
-      "x_20MW\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "tl.load_internal_library(which=[\"x_20MW.yaml\"])\n",
-    "for turbine in tl.turbine_map:\n",
-    "    print(turbine)"
+    "# tl.load_internal_library(which=[\"x_20MW.yaml\"])\n",
+    "# for turbine in tl.turbine_map:\n",
+    "#     print(turbine)\n",
+    "\n",
+    "# TODO Removed until 20MW turbine is updated to v4"
    ]
   },
   {
@@ -344,7 +333,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x1600 with 4 Axes>"
       ]
@@ -395,26 +384,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                  Turbine | Rotor Diameter (m) | Hub Height (m) | Air Density (ρ)\n",
-      "---------------------------------------------------------------------------------\n",
-      "        iea_15MW_floating |             242.24 |          150.0 |           1.225\n",
-      "                 iea_10MW |             198.00 |          119.0 |           1.225\n",
-      "                 iea_15MW |             242.24 |          150.0 |           1.225\n",
-      " iea_15MW_multi_dim_cp_ct |             242.24 |          150.0 |           1.225\n",
-      "                 nrel_5MW |             126.00 |           90.0 |           1.225\n",
-      "                   x_20MW |             252.00 |          165.0 |           1.225\n"
+      "                  Turbine | Efficiency | Rotor Diameter (m) | Hub Height (m) |    TSR | Air Density (ρ) | Tilt (º)\n",
+      "------------------------------------------------------------------------------------------------------------------\n",
+      "        iea_15MW_floating |       1.00 |             242.24 |          150.0 |    8.0 |           1.225 |    6.000\n",
+      " iea_15MW_multi_dim_cp_ct |       1.00 |             242.24 |          150.0 |    8.0 |           1.225 |    6.000\n",
+      "                 iea_15MW |       1.00 |             242.24 |          150.0 |    8.0 |           1.225 |    6.000\n",
+      "                 nrel_5MW |       1.00 |             126.00 |           90.0 |    8.0 |           1.225 |    5.000\n",
+      "                 iea_10MW |       1.00 |             198.00 |          119.0 |    8.0 |           1.225 |    6.000\n"
      ]
     }
    ],
    "source": [
-    "header = f\"{'Turbine':>25} | Rotor Diameter (m) | Hub Height (m) | Air Density (ρ)\"\n",
+    "header = f\"\\\n",
+    "{'Turbine':>25} | \\\n",
+    "{'Efficiency':>10} | \\\n",
+    "{'Rotor Diameter (m)':>18} | \\\n",
+    "{'Hub Height (m)':>14} | \\\n",
+    "{'TSR':>6} | \\\n",
+    "{'Air Density (ρ)':>15} | \\\n",
+    "{'Tilt (º)':>8}\\\n",
+    "\"\n",
     "print(header)\n",
     "print(\"-\" * len(header))\n",
     "for name, t in tl.turbine_map.items():\n",
     "    print(f\"{name:>25}\", end=\" | \")\n",
+    "    print(f\"{t.turbine.generator_efficiency:>10,.2f}\", end=\" | \")\n",
     "    print(f\"{t.turbine.rotor_diameter:>18,.2f}\", end=\" | \")\n",
     "    print(f\"{t.turbine.hub_height:>14,.1f}\", end=\" | \")\n",
-    "    print(f\"{t.turbine.ref_air_density:>15,.3f}\")"
+    "    print(f\"{t.turbine.TSR:>6,.1f}\", end=\" | \")\n",
+    "    if t.turbine.multi_dimensional_cp_ct:\n",
+    "        condition_keys = list(t.turbine.power_thrust_table.keys())\n",
+    "        print(f\"{t.turbine.power_thrust_table[condition_keys[0]]['ref_air_density']:>15,.3f}\", end=\" | \")\n",
+    "        print(f\"{t.turbine.power_thrust_table[condition_keys[0]]['ref_tilt']:>8,.3f}\")\n",
+    "    else:\n",
+    "        print(f\"{t.turbine.power_thrust_table['ref_air_density']:>15,.3f}\", end=\" | \")\n",
+    "        print(f\"{t.turbine.power_thrust_table['ref_tilt']:>8,.3f}\")"
    ]
   }
  ],
diff --git a/examples/18_check_turbine.py b/examples/18_check_turbine.py
index cb7a951d1..738cfa8c1 100644
--- a/examples/18_check_turbine.py
+++ b/examples/18_check_turbine.py
@@ -49,7 +49,14 @@
 
 # TEMPORARY
 print(turbines)
-turbines = turbines[1:]
+turbines = [
+    t for t in turbines
+    if "converted" not in t
+    if "updated" not in t
+    if "legacy" not in t
+    if t != "x_20MW"
+]
+print(turbines)
 # END TEMPORARY
 
 # Declare a set of figures for comparing cp and ct across models
diff --git a/examples/24_floating_turbine_models.py b/examples/24_floating_turbine_models.py
index 863b896a4..c94fbf538 100644
--- a/examples/24_floating_turbine_models.py
+++ b/examples/24_floating_turbine_models.py
@@ -67,9 +67,11 @@
 power_floating_defined_floating = fi_floating_defined_floating.get_turbine_powers().flatten()/1000.
 
 # Grab Ct
-ct_fixed = fi_fixed.get_turbine_Cts().flatten()
-ct_floating = fi_floating.get_turbine_Cts().flatten()
-ct_floating_defined_floating = fi_floating_defined_floating.get_turbine_Cts().flatten()
+ct_fixed = fi_fixed.get_turbine_thrust_coefficients().flatten()
+ct_floating = fi_floating.get_turbine_thrust_coefficients().flatten()
+ct_floating_defined_floating = (
+    fi_floating_defined_floating.get_turbine_thrust_coefficients().flatten()
+)
 
 # Grab turbine tilt angles
 eff_vels = fi_fixed.turbine_average_velocities
diff --git a/examples/30_multi_dimensional_cp_ct.py b/examples/30_multi_dimensional_cp_ct.py
index 5de69d014..05df42c0f 100644
--- a/examples/30_multi_dimensional_cp_ct.py
+++ b/examples/30_multi_dimensional_cp_ct.py
@@ -72,7 +72,7 @@
 fi.calculate_wake(yaw_angles=yaw_angles)
 
 # Get the turbine powers
-turbine_powers = fi.get_turbine_powers_multidim() / 1000.0
+turbine_powers = fi.get_turbine_powers() / 1000.0
 print("The turbine power matrix should be of dimensions 1 findex X 2 Turbines")
 print(turbine_powers)
 print("Shape: ",turbine_powers.shape)
@@ -86,7 +86,7 @@
 fi.reinitialize(wind_speeds=wind_speeds, wind_directions=wind_directions)
 yaw_angles = np.zeros([3, 2])  # 3 wind directions/ speeds, 2 turbines
 fi.calculate_wake(yaw_angles=yaw_angles)
-turbine_powers = fi.get_turbine_powers_multidim() / 1000.0
+turbine_powers = fi.get_turbine_powers() / 1000.0
 print("The turbine power matrix should be of dimensions 3 findex X 2 Turbines")
 print(turbine_powers)
 print("Shape: ",turbine_powers.shape)
@@ -100,7 +100,7 @@
 fi.reinitialize(wind_directions=wind_directions, wind_speeds=wind_speeds)
 yaw_angles = np.zeros([9, 2])  # 9 wind directions/ speeds, 2 turbines
 fi.calculate_wake(yaw_angles=yaw_angles)
-turbine_powers = fi.get_turbine_powers_multidim()/1000.
+turbine_powers = fi.get_turbine_powers()/1000.
 print("The turbine power matrix should be of dimensions 9 WD/WS X 2 Turbines")
 print(turbine_powers)
 print("Shape: ",turbine_powers.shape)
diff --git a/examples/31_multi_dimensional_cp_ct_2Hs.py b/examples/31_multi_dimensional_cp_ct_2Hs.py
index 9726fda61..57be38fc0 100644
--- a/examples/31_multi_dimensional_cp_ct_2Hs.py
+++ b/examples/31_multi_dimensional_cp_ct_2Hs.py
@@ -56,8 +56,8 @@
 fi_hs_1.calculate_wake()
 
 # Collect the turbine powers in kW
-turbine_powers = fi.get_turbine_powers_multidim()/1000.
-turbine_powers_hs_1 = fi_hs_1.get_turbine_powers_multidim()/1000.
+turbine_powers = fi.get_turbine_powers()/1000.
+turbine_powers_hs_1 = fi_hs_1.get_turbine_powers()/1000.
 
 # Plot the power in each case and the difference in power
 fig, axarr = plt.subplots(1,3,sharex=True,figsize=(12,4))
diff --git a/examples/33_specify_turbine_power_curve.py b/examples/33_specify_turbine_power_curve.py
index 8d80db8a6..870bbde1b 100644
--- a/examples/33_specify_turbine_power_curve.py
+++ b/examples/33_specify_turbine_power_curve.py
@@ -16,8 +16,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from floris.simulation import turbine
-from floris.tools import build_turbine_dict, FlorisInterface
+from floris.tools import FlorisInterface
+from floris.turbine_library import build_cosine_loss_turbine_dict
 
 
 """
@@ -39,7 +39,7 @@
     "thrust_coefficient":[0, 0.9, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.25, 0.2]
 }
 
-turbine_dict = build_turbine_dict(
+turbine_dict = build_cosine_loss_turbine_dict(
     turbine_data_dict,
     "example_turbine",
     file_name=None,
@@ -70,7 +70,7 @@
 
 specified_powers = (
     np.array(turbine_data_dict["power_coefficient"])
-    *0.5*turbine_dict["ref_air_density"]
+    *0.5*turbine_dict["power_thrust_table"]["ref_air_density"]
     *turbine_dict["rotor_diameter"]**2*np.pi/4
     *np.array(turbine_data_dict["wind_speed"])**3
 )/1000
diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml
index b1755ab6c..af36a9bfa 100644
--- a/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml
+++ b/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml
@@ -1,67 +1,67 @@
 turbine_type: 'nrel_5MW_floating'
 generator_efficiency: 1.0
 hub_height: 90.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 126.0
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 5.0
 correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 5.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.0
-    - 0.000000
-    - 0.000000
-    - 0.178085
-    - 0.289075
-    - 0.349022
-    - 0.384728
-    - 0.406059
-    - 0.420228
-    - 0.428823
-    - 0.433873
-    - 0.436223
-    - 0.436845
-    - 0.436575
-    - 0.436511
-    - 0.436561
-    - 0.436517
-    - 0.435903
-    - 0.434673
-    - 0.433230
-    - 0.430466
-    - 0.378869
-    - 0.335199
-    - 0.297991
-    - 0.266092
-    - 0.238588
-    - 0.214748
-    - 0.193981
-    - 0.175808
-    - 0.159835
-    - 0.145741
-    - 0.133256
-    - 0.122157
-    - 0.112257
-    - 0.103399
-    - 0.095449
-    - 0.088294
-    - 0.081836
-    - 0.075993
-    - 0.070692
-    - 0.065875
-    - 0.061484
-    - 0.057476
-    - 0.053809
-    - 0.050447
-    - 0.047358
-    - 0.044518
-    - 0.041900
-    - 0.039483
     - 0.0
     - 0.0
-  thrust:
+    - 36.722155848902254
+    - 94.65678115354163
+    - 170.596391826316
+    - 267.74933496419163
+    - 387.64681352354114
+    - 533.9617151673435
+    - 707.4062402827329
+    - 909.9965782677073
+    - 1142.7197798534328
+    - 1407.4994184495558
+    - 1707.1272243371227
+    - 2047.3355806543098
+    - 2430.5778091805637
+    - 2858.3081150622215
+    - 3329.100627354195
+    - 3842.9755943182267
+    - 4403.86140594055
+    - 4999.993508066915
+    - 4999.99850473839
+    - 4999.997854617397
+    - 5000.00304890274
+    - 5000.002113339491
+    - 4999.997282778227
+    - 5000.002243172759
+    - 5000.000360590384
+    - 5000.009074693787
+    - 4999.987262704901
+    - 5000.007345811091
+    - 5000.006875165497
+    - 4999.994990648268
+    - 4999.97705933755
+    - 4999.983698972648
+    - 4999.991318085188
+    - 5000.024022703328
+    - 5000.016589748782
+    - 5000.025709581146
+    - 4999.944891236294
+    - 5000.035324880168
+    - 4999.967955734346
+    - 5000.013248451465
+    - 5000.063199891701
+    - 5000.068982245371
+    - 4999.9325188896555
+    - 5000.011035557985
+    - 5000.012771123277
+    - 4717.243379938609
+    - 0.0
+    - 0.0
+  thrust_coefficient:
     - 0.0
     - 0.0
     - 0.0
diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml
index cf3bc3049..c2b9675de 100644
--- a/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml
+++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml
@@ -1,67 +1,67 @@
 turbine_type: 'nrel_5MW_floating'
 generator_efficiency: 1.0
 hub_height: 90.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 126.0
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 5.0
 correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 5.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.0
-    - 0.000000
-    - 0.000000
-    - 0.178085
-    - 0.289075
-    - 0.349022
-    - 0.384728
-    - 0.406059
-    - 0.420228
-    - 0.428823
-    - 0.433873
-    - 0.436223
-    - 0.436845
-    - 0.436575
-    - 0.436511
-    - 0.436561
-    - 0.436517
-    - 0.435903
-    - 0.434673
-    - 0.433230
-    - 0.430466
-    - 0.378869
-    - 0.335199
-    - 0.297991
-    - 0.266092
-    - 0.238588
-    - 0.214748
-    - 0.193981
-    - 0.175808
-    - 0.159835
-    - 0.145741
-    - 0.133256
-    - 0.122157
-    - 0.112257
-    - 0.103399
-    - 0.095449
-    - 0.088294
-    - 0.081836
-    - 0.075993
-    - 0.070692
-    - 0.065875
-    - 0.061484
-    - 0.057476
-    - 0.053809
-    - 0.050447
-    - 0.047358
-    - 0.044518
-    - 0.041900
-    - 0.039483
     - 0.0
     - 0.0
-  thrust:
+    - 36.722155848902254
+    - 94.65678115354163
+    - 170.596391826316
+    - 267.74933496419163
+    - 387.64681352354114
+    - 533.9617151673435
+    - 707.4062402827329
+    - 909.9965782677073
+    - 1142.7197798534328
+    - 1407.4994184495558
+    - 1707.1272243371227
+    - 2047.3355806543098
+    - 2430.5778091805637
+    - 2858.3081150622215
+    - 3329.100627354195
+    - 3842.9755943182267
+    - 4403.86140594055
+    - 4999.993508066915
+    - 4999.99850473839
+    - 4999.997854617397
+    - 5000.00304890274
+    - 5000.002113339491
+    - 4999.997282778227
+    - 5000.002243172759
+    - 5000.000360590384
+    - 5000.009074693787
+    - 4999.987262704901
+    - 5000.007345811091
+    - 5000.006875165497
+    - 4999.994990648268
+    - 4999.97705933755
+    - 4999.983698972648
+    - 4999.991318085188
+    - 5000.024022703328
+    - 5000.016589748782
+    - 5000.025709581146
+    - 4999.944891236294
+    - 5000.035324880168
+    - 4999.967955734346
+    - 5000.013248451465
+    - 5000.063199891701
+    - 5000.068982245371
+    - 4999.9325188896555
+    - 5000.011035557985
+    - 5000.012771123277
+    - 4717.243379938609
+    - 0.0
+    - 0.0
+  thrust_coefficient:
     - 0.0
     - 0.0
     - 0.0
diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml
index 4fa506e25..ee8232b2c 100644
--- a/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml
+++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml
@@ -1,67 +1,67 @@
 turbine_type: 'nrel_5MW_floating'
 generator_efficiency: 1.0
 hub_height: 90.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 126.0
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 5.0
 correct_cp_ct_for_tilt: False # Do not apply tilt correction to cp/ct
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 5.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.0
-    - 0.000000
-    - 0.000000
-    - 0.178085
-    - 0.289075
-    - 0.349022
-    - 0.384728
-    - 0.406059
-    - 0.420228
-    - 0.428823
-    - 0.433873
-    - 0.436223
-    - 0.436845
-    - 0.436575
-    - 0.436511
-    - 0.436561
-    - 0.436517
-    - 0.435903
-    - 0.434673
-    - 0.433230
-    - 0.430466
-    - 0.378869
-    - 0.335199
-    - 0.297991
-    - 0.266092
-    - 0.238588
-    - 0.214748
-    - 0.193981
-    - 0.175808
-    - 0.159835
-    - 0.145741
-    - 0.133256
-    - 0.122157
-    - 0.112257
-    - 0.103399
-    - 0.095449
-    - 0.088294
-    - 0.081836
-    - 0.075993
-    - 0.070692
-    - 0.065875
-    - 0.061484
-    - 0.057476
-    - 0.053809
-    - 0.050447
-    - 0.047358
-    - 0.044518
-    - 0.041900
-    - 0.039483
     - 0.0
     - 0.0
-  thrust:
+    - 36.722155848902254
+    - 94.65678115354163
+    - 170.596391826316
+    - 267.74933496419163
+    - 387.64681352354114
+    - 533.9617151673435
+    - 707.4062402827329
+    - 909.9965782677073
+    - 1142.7197798534328
+    - 1407.4994184495558
+    - 1707.1272243371227
+    - 2047.3355806543098
+    - 2430.5778091805637
+    - 2858.3081150622215
+    - 3329.100627354195
+    - 3842.9755943182267
+    - 4403.86140594055
+    - 4999.993508066915
+    - 4999.99850473839
+    - 4999.997854617397
+    - 5000.00304890274
+    - 5000.002113339491
+    - 4999.997282778227
+    - 5000.002243172759
+    - 5000.000360590384
+    - 5000.009074693787
+    - 4999.987262704901
+    - 5000.007345811091
+    - 5000.006875165497
+    - 4999.994990648268
+    - 4999.97705933755
+    - 4999.983698972648
+    - 4999.991318085188
+    - 5000.024022703328
+    - 5000.016589748782
+    - 5000.025709581146
+    - 4999.944891236294
+    - 5000.035324880168
+    - 4999.967955734346
+    - 5000.013248451465
+    - 5000.063199891701
+    - 5000.068982245371
+    - 4999.9325188896555
+    - 5000.011035557985
+    - 5000.012771123277
+    - 4717.243379938609
+    - 0.0
+    - 0.0
+  thrust_coefficient:
     - 0.0
     - 0.0
     - 0.0
diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml
index da0d15a37..60460f641 100644
--- a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml
+++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml
@@ -1,67 +1,67 @@
 turbine_type: 'nrel_5MW_floating'
 generator_efficiency: 1.0
 hub_height: 90.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 126.0
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 5.0
 correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 5.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.0
-    - 0.000000
-    - 0.000000
-    - 0.178085
-    - 0.289075
-    - 0.349022
-    - 0.384728
-    - 0.406059
-    - 0.420228
-    - 0.428823
-    - 0.433873
-    - 0.436223
-    - 0.436845
-    - 0.436575
-    - 0.436511
-    - 0.436561
-    - 0.436517
-    - 0.435903
-    - 0.434673
-    - 0.433230
-    - 0.430466
-    - 0.378869
-    - 0.335199
-    - 0.297991
-    - 0.266092
-    - 0.238588
-    - 0.214748
-    - 0.193981
-    - 0.175808
-    - 0.159835
-    - 0.145741
-    - 0.133256
-    - 0.122157
-    - 0.112257
-    - 0.103399
-    - 0.095449
-    - 0.088294
-    - 0.081836
-    - 0.075993
-    - 0.070692
-    - 0.065875
-    - 0.061484
-    - 0.057476
-    - 0.053809
-    - 0.050447
-    - 0.047358
-    - 0.044518
-    - 0.041900
-    - 0.039483
     - 0.0
     - 0.0
-  thrust:
+    - 36.722155848902254
+    - 94.65678115354163
+    - 170.596391826316
+    - 267.74933496419163
+    - 387.64681352354114
+    - 533.9617151673435
+    - 707.4062402827329
+    - 909.9965782677073
+    - 1142.7197798534328
+    - 1407.4994184495558
+    - 1707.1272243371227
+    - 2047.3355806543098
+    - 2430.5778091805637
+    - 2858.3081150622215
+    - 3329.100627354195
+    - 3842.9755943182267
+    - 4403.86140594055
+    - 4999.993508066915
+    - 4999.99850473839
+    - 4999.997854617397
+    - 5000.00304890274
+    - 5000.002113339491
+    - 4999.997282778227
+    - 5000.002243172759
+    - 5000.000360590384
+    - 5000.009074693787
+    - 4999.987262704901
+    - 5000.007345811091
+    - 5000.006875165497
+    - 4999.994990648268
+    - 4999.97705933755
+    - 4999.983698972648
+    - 4999.991318085188
+    - 5000.024022703328
+    - 5000.016589748782
+    - 5000.025709581146
+    - 4999.944891236294
+    - 5000.035324880168
+    - 4999.967955734346
+    - 5000.013248451465
+    - 5000.063199891701
+    - 5000.068982245371
+    - 4999.9325188896555
+    - 5000.011035557985
+    - 5000.012771123277
+    - 4717.243379938609
+    - 0.0
+    - 0.0
+  thrust_coefficient:
     - 0.0
     - 0.0
     - 0.0
diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml
index b1755ab6c..af36a9bfa 100644
--- a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml
+++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml
@@ -1,67 +1,67 @@
 turbine_type: 'nrel_5MW_floating'
 generator_efficiency: 1.0
 hub_height: 90.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 126.0
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 5.0
 correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 5.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.0
-    - 0.000000
-    - 0.000000
-    - 0.178085
-    - 0.289075
-    - 0.349022
-    - 0.384728
-    - 0.406059
-    - 0.420228
-    - 0.428823
-    - 0.433873
-    - 0.436223
-    - 0.436845
-    - 0.436575
-    - 0.436511
-    - 0.436561
-    - 0.436517
-    - 0.435903
-    - 0.434673
-    - 0.433230
-    - 0.430466
-    - 0.378869
-    - 0.335199
-    - 0.297991
-    - 0.266092
-    - 0.238588
-    - 0.214748
-    - 0.193981
-    - 0.175808
-    - 0.159835
-    - 0.145741
-    - 0.133256
-    - 0.122157
-    - 0.112257
-    - 0.103399
-    - 0.095449
-    - 0.088294
-    - 0.081836
-    - 0.075993
-    - 0.070692
-    - 0.065875
-    - 0.061484
-    - 0.057476
-    - 0.053809
-    - 0.050447
-    - 0.047358
-    - 0.044518
-    - 0.041900
-    - 0.039483
     - 0.0
     - 0.0
-  thrust:
+    - 36.722155848902254
+    - 94.65678115354163
+    - 170.596391826316
+    - 267.74933496419163
+    - 387.64681352354114
+    - 533.9617151673435
+    - 707.4062402827329
+    - 909.9965782677073
+    - 1142.7197798534328
+    - 1407.4994184495558
+    - 1707.1272243371227
+    - 2047.3355806543098
+    - 2430.5778091805637
+    - 2858.3081150622215
+    - 3329.100627354195
+    - 3842.9755943182267
+    - 4403.86140594055
+    - 4999.993508066915
+    - 4999.99850473839
+    - 4999.997854617397
+    - 5000.00304890274
+    - 5000.002113339491
+    - 4999.997282778227
+    - 5000.002243172759
+    - 5000.000360590384
+    - 5000.009074693787
+    - 4999.987262704901
+    - 5000.007345811091
+    - 5000.006875165497
+    - 4999.994990648268
+    - 4999.97705933755
+    - 4999.983698972648
+    - 4999.991318085188
+    - 5000.024022703328
+    - 5000.016589748782
+    - 5000.025709581146
+    - 4999.944891236294
+    - 5000.035324880168
+    - 4999.967955734346
+    - 5000.013248451465
+    - 5000.063199891701
+    - 5000.068982245371
+    - 4999.9325188896555
+    - 5000.011035557985
+    - 5000.012771123277
+    - 4717.243379938609
+    - 0.0
+    - 0.0
+  thrust_coefficient:
     - 0.0
     - 0.0
     - 0.0
diff --git a/floris/simulation/__init__.py b/floris/simulation/__init__.py
index b7b41ed16..2182951ca 100644
--- a/floris/simulation/__init__.py
+++ b/floris/simulation/__init__.py
@@ -37,19 +37,16 @@
 import floris.logging_manager
 
 from .base import BaseClass, BaseModel, State
-from .turbine import (
-    average_velocity,
+from .turbine.turbine import (
     axial_induction,
-    compute_tilt_angles_for_floating_turbines,
-    Ct,
     power,
-    rotor_effective_velocity,
+    thrust_coefficient,
     Turbine
 )
-from .turbine_multi_dim import (
-    axial_induction_multidim,
-    Ct_multidim,
-    TurbineMultiDimensional
+from .rotor_velocity import (
+    average_velocity,
+    rotor_effective_velocity,
+    compute_tilt_angles_for_floating_turbines,
 )
 from .farm import Farm
 from .grid import (
@@ -70,7 +67,6 @@
     full_flow_sequential_solver,
     full_flow_turbopark_solver,
     sequential_solver,
-    sequential_multidim_solver,
     turbopark_solver,
 )
 from .floris import Floris
diff --git a/floris/simulation/farm.py b/floris/simulation/farm.py
index 0b58cc936..7544231fe 100644
--- a/floris/simulation/farm.py
+++ b/floris/simulation/farm.py
@@ -13,6 +13,7 @@
 from __future__ import annotations
 
 import copy
+from collections.abc import Callable
 from pathlib import Path
 from typing import (
     Any,
@@ -29,9 +30,8 @@
     BaseClass,
     State,
     Turbine,
-    TurbineMultiDimensional,
 )
-from floris.simulation.turbine import compute_tilt_angles_for_floating_turbines
+from floris.simulation.rotor_velocity import compute_tilt_angles_for_floating_turbines_map
 from floris.type_dec import (
     convert_to_path,
     floris_array_converter,
@@ -81,8 +81,8 @@ class Farm(BaseClass):
 
     turbine_definitions: list = field(init=False, validator=iter_validator(list, dict))
 
-    turbine_fCts: Dict[str, interp1d] | List[interp1d] = field(init=False, factory=list)
-    turbine_fCts_sorted: NDArrayFloat = field(init=False, factory=list)
+    turbine_thrust_coefficient_functions: Dict[str, Callable] = field(init=False, factory=list)
+    turbine_axial_induction_functions: Dict[str, Callable] = field(init=False, factory=list)
 
     turbine_tilt_interps: dict[str, interp1d] = field(init=False, factory=dict)
 
@@ -95,13 +95,13 @@ class Farm(BaseClass):
     hub_heights: NDArrayFloat = field(init=False)
     hub_heights_sorted: NDArrayFloat = field(init=False, factory=list)
 
-    turbine_map: List[Turbine | TurbineMultiDimensional] = field(init=False, factory=list)
+    turbine_map: List[Turbine] = field(init=False, factory=list)
 
     turbine_type_map: NDArrayObject = field(init=False, factory=list)
     turbine_type_map_sorted: NDArrayObject = field(init=False, factory=list)
 
-    turbine_power_interps: Dict[str, interp1d] | List[interp1d] = field(init=False, factory=list)
-    turbine_power_interps_sorted: NDArrayFloat = field(init=False, factory=list)
+    turbine_power_functions: Dict[str, Callable] = field(init=False, factory=list)
+    turbine_power_thrust_tables: Dict[str, dict] = field(init=False, factory=list)
 
     rotor_diameters: NDArrayFloat = field(init=False, factory=list)
     rotor_diameters_sorted: NDArrayFloat = field(init=False, factory=list)
@@ -109,15 +109,6 @@ class Farm(BaseClass):
     TSRs: NDArrayFloat = field(init=False, factory=list)
     TSRs_sorted: NDArrayFloat = field(init=False, factory=list)
 
-    pPs: NDArrayFloat = field(init=False, factory=list)
-    pPs_sorted: NDArrayFloat = field(init=False, factory=list)
-
-    pTs: NDArrayFloat = field(init=False, factory=list)
-    pTs_sorted: NDArrayFloat = field(init=False, factory=list)
-
-    ref_air_densities: NDArrayFloat = field(init=False, factory=list)
-    ref_air_densities_sorted: NDArrayFloat = field(init=False, factory=list)
-
     ref_tilts: NDArrayFloat = field(init=False, factory=list)
     ref_tilts_sorted: NDArrayFloat = field(init=False, factory=list)
 
@@ -255,20 +246,9 @@ def construct_rotor_diameters(self):
     def construct_turbine_TSRs(self):
         self.TSRs = np.array([turb['TSR'] for turb in self.turbine_definitions])
 
-    def construct_turbine_pPs(self):
-        self.pPs = np.array([turb['pP'] for turb in self.turbine_definitions])
-
-    def construct_turbine_pTs(self):
-        self.pTs = np.array([turb['pT'] for turb in self.turbine_definitions])
-
-    def construct_turbine_ref_air_densities(self):
-        self.ref_air_densities = np.array([
-            turb['ref_air_density'] for turb in self.turbine_definitions
-        ])
-
     def construct_turbine_ref_tilts(self):
         self.ref_tilts = np.array(
-            [turb['ref_tilt'] for turb in self.turbine_definitions]
+            [turb['power_thrust_table']['ref_tilt'] for turb in self.turbine_definitions]
         )
 
     def construct_turbine_correct_cp_ct_for_tilt(self):
@@ -277,39 +257,32 @@ def construct_turbine_correct_cp_ct_for_tilt(self):
         )
 
     def construct_turbine_map(self):
-        multi_key = "multi_dimensional_cp_ct"
-        if multi_key in self.turbine_definitions[0] and self.turbine_definitions[0][multi_key]:
-            self.turbine_map = []
-            for turb in self.turbine_definitions:
-                _turb = {**turb, **{"turbine_library_path": self.internal_turbine_library}}
-                try:
-                    self.turbine_map.append(TurbineMultiDimensional.from_dict(_turb))
-                except FileNotFoundError:
-                    _turb["turbine_library_path"] = self.turbine_library_path
-                    self.turbine_map.append(TurbineMultiDimensional.from_dict(_turb))
-        else:
-            self.turbine_map = [Turbine.from_dict(turb) for turb in self.turbine_definitions]
-
-    def construct_turbine_fCts(self):
-        self.turbine_fCts = {
-            turb.turbine_type: turb.fCt_interp for turb in self.turbine_map
+        self.turbine_map = [Turbine.from_dict(turb) for turb in self.turbine_definitions]
+
+    def construct_turbine_thrust_coefficient_functions(self):
+        self.turbine_thrust_coefficient_functions = {
+            turb.turbine_type: turb.thrust_coefficient_function for turb in self.turbine_map
         }
 
-    def construct_multidim_turbine_fCts(self):
-        self.turbine_fCts = [turb.fCt_interp for turb in self.turbine_map]
+    def construct_turbine_axial_induction_functions(self):
+        self.turbine_axial_induction_functions = {
+            turb.turbine_type: turb.axial_induction_function for turb in self.turbine_map
+        }
 
     def construct_turbine_tilt_interps(self):
         self.turbine_tilt_interps = {
             turb.turbine_type: turb.tilt_interp for turb in self.turbine_map
         }
 
-    def construct_turbine_power_interps(self):
-        self.turbine_power_interps = {
-            turb.turbine_type: turb.power_interp for turb in self.turbine_map
+    def construct_turbine_power_functions(self):
+        self.turbine_power_functions = {
+            turb.turbine_type: turb.power_function for turb in self.turbine_map
         }
 
-    def construct_multidim_turbine_power_interps(self):
-        self.turbine_power_interps = [turb.power_interp for turb in self.turbine_map]
+    def construct_turbine_power_thrust_tables(self):
+        self.turbine_power_thrust_tables = {
+            turb.turbine_type: turb.power_thrust_table for turb in self.turbine_map
+        }
 
     def expand_farm_properties(self, n_findex: int, sorted_coord_indices):
         template_shape = np.ones_like(sorted_coord_indices)
@@ -318,26 +291,6 @@ def expand_farm_properties(self, n_findex: int, sorted_coord_indices):
             sorted_coord_indices,
             axis=1
         )
-        if 'multi_dimensional_cp_ct' in self.turbine_definitions[0].keys() \
-            and self.turbine_definitions[0]['multi_dimensional_cp_ct'] is True:
-            findex_dim = np.shape(template_shape)[0]
-
-            self.turbine_fCts_sorted = np.take_along_axis(
-                np.reshape(
-                    np.repeat(self.turbine_fCts, findex_dim),
-                    np.shape(template_shape)
-                ),
-                sorted_coord_indices,
-                axis=1
-            )
-            self.turbine_power_interps_sorted = np.take_along_axis(
-                np.reshape(
-                    np.repeat(self.turbine_power_interps, findex_dim),
-                    np.shape(template_shape)
-                ),
-                sorted_coord_indices,
-                axis=1
-            )
         self.rotor_diameters_sorted = np.take_along_axis(
             self.rotor_diameters * template_shape,
             sorted_coord_indices,
@@ -348,11 +301,6 @@ def expand_farm_properties(self, n_findex: int, sorted_coord_indices):
             sorted_coord_indices,
             axis=1
         )
-        self.ref_air_densities_sorted = np.take_along_axis(
-            self.ref_air_densities * template_shape,
-            sorted_coord_indices,
-            axis=1
-        )
         self.ref_tilts_sorted = np.take_along_axis(
             self.ref_tilts * template_shape,
             sorted_coord_indices,
@@ -363,16 +311,6 @@ def expand_farm_properties(self, n_findex: int, sorted_coord_indices):
             sorted_coord_indices,
             axis=1
         )
-        self.pPs_sorted = np.take_along_axis(
-            self.pPs * template_shape,
-            sorted_coord_indices,
-            axis=1
-        )
-        self.pTs_sorted = np.take_along_axis(
-            self.pTs * template_shape,
-            sorted_coord_indices,
-            axis=1
-        )
 
         # NOTE: Tilt angles are sorted twice - here and in initialize()
         self.tilt_angles_sorted = np.take_along_axis(
@@ -404,7 +342,7 @@ def set_tilt_to_ref_tilt(self, n_findex: int):
         )
 
     def calculate_tilt_for_eff_velocities(self, rotor_effective_velocities):
-        tilt_angles = compute_tilt_angles_for_floating_turbines(
+        tilt_angles = compute_tilt_angles_for_floating_turbines_map(
             self.turbine_type_map_sorted,
             self.tilt_angles_sorted,
             self.turbine_tilt_interps,
@@ -413,18 +351,6 @@ def calculate_tilt_for_eff_velocities(self, rotor_effective_velocities):
         return tilt_angles
 
     def finalize(self, unsorted_indices):
-        if 'multi_dimensional_cp_ct' in self.turbine_definitions[0].keys() \
-            and self.turbine_definitions[0]['multi_dimensional_cp_ct'] is True:
-            self.turbine_fCts = np.take_along_axis(
-                self.turbine_fCts_sorted,
-                unsorted_indices[:,:,0,0],
-                axis=1
-            )
-            self.turbine_power_interps = np.take_along_axis(
-                self.turbine_power_interps_sorted,
-                unsorted_indices[:,:,0,0],
-                axis=1
-            )
         self.yaw_angles = np.take_along_axis(
             self.yaw_angles_sorted,
             unsorted_indices[:,:,0,0],
@@ -450,11 +376,6 @@ def finalize(self, unsorted_indices):
             unsorted_indices[:,:,0,0],
             axis=1
         )
-        self.ref_air_densities = np.take_along_axis(
-            self.ref_air_densities_sorted,
-            unsorted_indices[:,:,0,0],
-            axis=1
-        )
         self.ref_tilts = np.take_along_axis(
             self.ref_tilts_sorted,
             unsorted_indices[:,:,0,0],
@@ -465,16 +386,6 @@ def finalize(self, unsorted_indices):
             unsorted_indices[:,:,0,0],
             axis=1
         )
-        self.pPs = np.take_along_axis(
-            self.pPs_sorted,
-            unsorted_indices[:,:,0,0],
-            axis=1
-        )
-        self.pTs = np.take_along_axis(
-            self.pTs_sorted,
-            unsorted_indices[:,:,0,0],
-            axis=1
-        )
         self.turbine_type_map = np.take_along_axis(
             self.turbine_type_map_sorted,
             unsorted_indices[:,:,0,0],
diff --git a/floris/simulation/floris.py b/floris/simulation/floris.py
index b7eaf7b86..e2e475e0e 100644
--- a/floris/simulation/floris.py
+++ b/floris/simulation/floris.py
@@ -36,7 +36,6 @@
     full_flow_turbopark_solver,
     Grid,
     PointsGrid,
-    sequential_multidim_solver,
     sequential_solver,
     State,
     TurbineCubatureGrid,
@@ -87,18 +86,13 @@ def __attrs_post_init__(self) -> None:
 
         # Initialize farm quantities that depend on other objects
         self.farm.construct_turbine_map()
-        if self.wake.model_strings['velocity_model'] == 'multidim_cp_ct':
-            self.farm.construct_multidim_turbine_fCts()
-            self.farm.construct_multidim_turbine_power_interps()
-        else:
-            self.farm.construct_turbine_fCts()
-            self.farm.construct_turbine_power_interps()
+        self.farm.construct_turbine_thrust_coefficient_functions()
+        self.farm.construct_turbine_axial_induction_functions()
+        self.farm.construct_turbine_power_functions()
+        self.farm.construct_turbine_power_thrust_tables()
         self.farm.construct_hub_heights()
         self.farm.construct_rotor_diameters()
         self.farm.construct_turbine_TSRs()
-        self.farm.construct_turbine_pPs()
-        self.farm.construct_turbine_pTs()
-        self.farm.construct_turbine_ref_air_densities()
         self.farm.construct_turbine_ref_tilts()
         self.farm.construct_turbine_tilt_interps()
         self.farm.construct_turbine_correct_cp_ct_for_tilt()
@@ -177,8 +171,8 @@ def steady_state_atmospheric_condition(self):
             self.farm.correct_cp_ct_for_tilt.any():
             self.logger.warning(
                 "The current model does not account for vertical wake deflection due to " +
-                "tilt. Corrections to Cp and Ct can be included, but no vertical wake " +
-                "deflection will occur."
+                "tilt. Corrections to power and thrust coefficient can be included, but no " +
+                "vertical wake deflection will occur."
             )
 
         if vel_model=="cc":
@@ -202,13 +196,6 @@ def steady_state_atmospheric_condition(self):
                 self.grid,
                 self.wake
             )
-        elif vel_model=="multidim_cp_ct":
-            sequential_multidim_solver(
-                self.farm,
-                self.flow_field,
-                self.grid,
-                self.wake
-            )
         else:
             sequential_solver(
                 self.farm,
diff --git a/floris/simulation/rotor_velocity.py b/floris/simulation/rotor_velocity.py
new file mode 100644
index 000000000..25f94d55d
--- /dev/null
+++ b/floris/simulation/rotor_velocity.py
@@ -0,0 +1,244 @@
+# Copyright 2021 NREL
+
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+# See https://floris.readthedocs.io for documentation
+
+from __future__ import annotations
+
+import copy
+from collections.abc import Iterable
+
+import numpy as np
+from scipy.interpolate import interp1d
+
+from floris.type_dec import (
+    NDArrayBool,
+    NDArrayFilter,
+    NDArrayFloat,
+    NDArrayInt,
+    NDArrayObject,
+)
+from floris.utilities import cosd
+
+
+def rotor_velocity_yaw_correction(
+    pP: float,
+    yaw_angles: NDArrayFloat,
+    rotor_effective_velocities: NDArrayFloat,
+) -> NDArrayFloat:
+    # Compute the rotor effective velocity adjusting for yaw settings
+    pW = pP / 3.0  # Convert from pP to w
+    # TODO: cosine loss hard coded
+    rotor_effective_velocities = rotor_effective_velocities * cosd(yaw_angles) ** pW
+
+    return rotor_effective_velocities
+
+def rotor_velocity_tilt_correction(
+    tilt_angles: NDArrayFloat,
+    ref_tilt: NDArrayFloat,
+    pT: float,
+    tilt_interp: NDArrayObject,
+    correct_cp_ct_for_tilt: NDArrayBool,
+    rotor_effective_velocities: NDArrayFloat,
+) -> NDArrayFloat:
+    # Compute the tilt, if using floating turbines
+    old_tilt_angle = copy.deepcopy(tilt_angles)
+    tilt_angles = compute_tilt_angles_for_floating_turbines(
+        tilt_angles,
+        tilt_interp,
+        rotor_effective_velocities,
+    )
+    # Only update tilt angle if requested (if the tilt isn't accounted for in the Cp curve)
+    tilt_angles = np.where(correct_cp_ct_for_tilt, tilt_angles, old_tilt_angle)
+
+    # Compute the rotor effective velocity adjusting for tilt
+    # TODO: cosine loss hard coded
+    relative_tilt = tilt_angles - ref_tilt
+    rotor_effective_velocities = rotor_effective_velocities * cosd(relative_tilt) ** (pT / 3.0)
+    return rotor_effective_velocities
+
+def simple_mean(array, axis=0):
+    return np.mean(array, axis=axis)
+
+def cubic_mean(array, axis=0):
+    return np.cbrt(np.mean(array ** 3.0, axis=axis))
+
+def simple_cubature(array, cubature_weights, axis=0):
+    weights = cubature_weights.flatten()
+    weights = weights * len(weights) / np.sum(weights)
+    product = (array * weights[None, None, :, None])
+    return simple_mean(product, axis)
+
+def cubic_cubature(array, cubature_weights, axis=0):
+    weights = cubature_weights.flatten()
+    weights = weights * len(weights) / np.sum(weights)
+    return np.cbrt(np.mean((array**3.0 * weights[None, None, :, None]), axis=axis))
+
+def average_velocity(
+    velocities: NDArrayFloat,
+    ix_filter: NDArrayFilter | Iterable[int] | None = None,
+    method: str = "cubic-mean",
+    cubature_weights: NDArrayFloat | None = None
+) -> NDArrayFloat:
+    """This property calculates and returns the average of the velocity field
+    in turbine's rotor swept area. The average is calculated using the
+    user-specified method. This is a vectorized function, so it can be used
+    to calculate the average velocity for multiple turbines at once or
+    a single turbine.
+
+    **Note:** The velocity is scaled to an effective velocity by the yaw.
+
+    Args:
+        velocities (NDArrayFloat): The velocity field at each turbine; should be shape:
+            (number of turbines, ngrid, ngrid), or (ngrid, ngrid) for a single turbine.
+        ix_filter (NDArrayFilter | Iterable[int] | None], optional): The boolean array, or
+            integer indices (as an iterable or array) to filter out before calculation.
+            Defaults to None.
+        method (str, optional): The method to use for averaging. Options are:
+            - "simple-mean": The simple mean of the velocities
+            - "cubic-mean": The cubic mean of the velocities
+            - "simple-cubature": A cubature integration of the velocities
+            - "cubic-cubature": A cubature integration of the cube of the velocities
+            Defaults to "cubic-mean".
+        cubature_weights (NDArrayFloat, optional): The cubature weights to use for the
+            cubature integration methods. Defaults to None.
+
+    Returns:
+        NDArrayFloat: The average velocity across the rotor(s).
+    """
+
+    # The input velocities are expected to be a 4 dimensional array with shape:
+    # (# findex, # turbines, grid resolution, grid resolution)
+
+    if ix_filter is not None:
+        velocities = velocities[:, ix_filter]
+
+    axis = tuple([2 + i for i in range(velocities.ndim - 2)])
+    if method == "simple-mean":
+        return simple_mean(velocities, axis)
+
+    elif method == "cubic-mean":
+        return cubic_mean(velocities, axis)
+
+    elif method == "simple-cubature":
+        if cubature_weights is None:
+            raise ValueError("cubature_weights is required for 'simple-cubature' method.")
+        return simple_cubature(velocities, cubature_weights, axis)
+
+    elif method == "cubic-cubature":
+        if cubature_weights is None:
+            raise ValueError("cubature_weights is required for 'cubic-cubature' method.")
+        return cubic_cubature(velocities, cubature_weights, axis)
+
+    else:
+        raise ValueError("Incorrect method given.")
+
+def compute_tilt_angles_for_floating_turbines_map(
+    turbine_type_map: NDArrayObject,
+    tilt_angles: NDArrayFloat,
+    tilt_interps: dict[str, interp1d],
+    rotor_effective_velocities: NDArrayFloat,
+) -> NDArrayFloat:
+    # Loop over each turbine type given to get tilt angles for all turbines
+    old_tilt_angles = copy.deepcopy(tilt_angles)
+    tilt_angles = np.zeros(np.shape(rotor_effective_velocities))
+    turb_types = np.unique(turbine_type_map)
+    for turb_type in turb_types:
+        # If no tilt interpolation is specified, assume no modification to tilt
+        if tilt_interps[turb_type] is None: # Use passed tilt angles
+            tilt_angles += old_tilt_angles * (turbine_type_map == turb_type)
+        else: # Apply interpolated tilt angle
+            tilt_angles += compute_tilt_angles_for_floating_turbines(
+                tilt_angles,
+                tilt_interps[turb_type],
+                rotor_effective_velocities
+            ) * (turbine_type_map == turb_type)
+
+    return tilt_angles
+
+def compute_tilt_angles_for_floating_turbines(
+    tilt_angles: NDArrayFloat,
+    tilt_interp: dict[str, interp1d],
+    rotor_effective_velocities: NDArrayFloat,
+) -> NDArrayFloat:
+    # Loop over each turbine type given to get tilt angles for all turbines
+    # If no tilt interpolation is specified, assume no modification to tilt
+    if tilt_interp is None:
+        # TODO should this be break? Should it be continue? Do we want to support mixed
+        # fixed-bottom and floating? Or non-tilting floating?
+        pass
+    # Using a masked array, apply the tilt angle for all turbines of the current
+    # type to the main tilt angle array
+    else:
+        tilt_angles = tilt_interp(rotor_effective_velocities)
+
+    return tilt_angles
+
+def rotor_effective_velocity(
+    air_density: float,
+    ref_air_density: float,
+    velocities: NDArrayFloat,
+    yaw_angle: NDArrayFloat,
+    tilt_angle: NDArrayFloat,
+    ref_tilt: NDArrayFloat,
+    pP: float,
+    pT: float,
+    tilt_interp: NDArrayObject,
+    correct_cp_ct_for_tilt: NDArrayBool,
+    turbine_type_map: NDArrayObject,
+    ix_filter: NDArrayInt | Iterable[int] | None = None,
+    average_method: str = "cubic-mean",
+    cubature_weights: NDArrayFloat | None = None
+) -> NDArrayFloat:
+
+    if isinstance(yaw_angle, list):
+        yaw_angle = np.array(yaw_angle)
+    if isinstance(tilt_angle, list):
+        tilt_angle = np.array(tilt_angle)
+
+    # Down-select inputs if ix_filter is given
+    if ix_filter is not None:
+        velocities = velocities[:, ix_filter]
+        yaw_angle = yaw_angle[:, ix_filter]
+        tilt_angle = tilt_angle[:, ix_filter]
+        ref_tilt = ref_tilt[:, ix_filter]
+        pP = pP[:, ix_filter]
+        pT = pT[:, ix_filter]
+        turbine_type_map = turbine_type_map[:, ix_filter]
+
+    # Compute the rotor effective velocity adjusting for air density
+    average_velocities = average_velocity(
+        velocities,
+        method=average_method,
+        cubature_weights=cubature_weights
+    )
+    rotor_effective_velocities = (air_density/ref_air_density)**(1/3) * average_velocities
+
+    # Compute the rotor effective velocity adjusting for yaw settings
+    rotor_effective_velocities = rotor_velocity_yaw_correction(
+        pP,
+        yaw_angle,
+        rotor_effective_velocities
+    )
+
+    # Compute the tilt, if using floating turbines
+    rotor_effective_velocities = rotor_velocity_tilt_correction(
+        turbine_type_map,
+        tilt_angle,
+        ref_tilt,
+        pT,
+        tilt_interp,
+        correct_cp_ct_for_tilt,
+        rotor_effective_velocities,
+    )
+
+    return rotor_effective_velocities
diff --git a/floris/simulation/solver.py b/floris/simulation/solver.py
index 54872d88a..d32ef9d15 100644
--- a/floris/simulation/solver.py
+++ b/floris/simulation/solver.py
@@ -18,20 +18,15 @@
 
 from floris.simulation import (
     axial_induction,
-    Ct,
     Farm,
     FlowField,
     FlowFieldGrid,
     FlowFieldPlanarGrid,
     PointsGrid,
+    thrust_coefficient,
     TurbineGrid,
 )
-from floris.simulation.turbine import average_velocity
-from floris.simulation.turbine_multi_dim import (
-    axial_induction_multidim,
-    Ct_multidim,
-    multidim_Ct_down_select,
-)
+from floris.simulation.rotor_velocity import average_velocity
 from floris.simulation.wake import WakeModelManager
 from floris.simulation.wake_deflection.empirical_gauss import yaw_added_wake_mixing
 from floris.simulation.wake_deflection.gauss import (
@@ -101,34 +96,36 @@ def sequential_solver(
         u_i = flow_field.u_sorted[:, i:i+1]
         v_i = flow_field.v_sorted[:, i:i+1]
 
-        ct_i = Ct(
+        ct_i = thrust_coefficient(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            thrust_coefficient_functions=farm.turbine_thrust_coefficient_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
-            cubature_weights=grid.cubature_weights
+            cubature_weights=grid.cubature_weights,
+            multidim_condition=flow_field.multidim_conditions
         )
-        # Since we are filtering for the i'th turbine in the Ct function,
+        # Since we are filtering for the i'th turbine in the thrust coefficient function,
         # get the first index here (0:1)
         ct_i = ct_i[:, 0:1, None, None]
         axial_induction_i = axial_induction(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            axial_induction_functions=farm.turbine_axial_induction_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
-            cubature_weights=grid.cubature_weights
+            cubature_weights=grid.cubature_weights,
+            multidim_condition=flow_field.multidim_conditions
         )
         # Since we are filtering for the i'th turbine in the axial induction function,
         # get the first index here (0:1)
@@ -273,14 +270,12 @@ def full_flow_sequential_solver(
     turbine_grid_flow_field = copy.deepcopy(flow_field)
 
     turbine_grid_farm.construct_turbine_map()
-    turbine_grid_farm.construct_turbine_fCts()
-    turbine_grid_farm.construct_turbine_power_interps()
+    turbine_grid_farm.construct_turbine_thrust_coefficient_functions()
+    turbine_grid_farm.construct_turbine_axial_induction_functions()
+    turbine_grid_farm.construct_turbine_power_functions()
     turbine_grid_farm.construct_hub_heights()
     turbine_grid_farm.construct_rotor_diameters()
     turbine_grid_farm.construct_turbine_TSRs()
-    turbine_grid_farm.construct_turbine_pPs()
-    turbine_grid_farm.construct_turbine_pTs()
-    turbine_grid_farm.construct_turbine_ref_air_densities()
     turbine_grid_farm.construct_turbine_ref_tilts()
     turbine_grid_farm.construct_turbine_tilt_interps()
     turbine_grid_farm.construct_turbine_correct_cp_ct_for_tilt()
@@ -331,29 +326,29 @@ def full_flow_sequential_solver(
         u_i = turbine_grid_flow_field.u_sorted[:, i:i+1]
         v_i = turbine_grid_flow_field.v_sorted[:, i:i+1]
 
-        ct_i = Ct(
+        ct_i = thrust_coefficient(
             velocities=turbine_grid_flow_field.u_sorted,
-            yaw_angle=turbine_grid_farm.yaw_angles_sorted,
-            tilt_angle=turbine_grid_farm.tilt_angles_sorted,
-            ref_tilt=turbine_grid_farm.ref_tilts_sorted,
-            fCt=turbine_grid_farm.turbine_fCts,
-            tilt_interp=turbine_grid_farm.turbine_tilt_interps,
+            yaw_angles=turbine_grid_farm.yaw_angles_sorted,
+            tilt_angles=turbine_grid_farm.tilt_angles_sorted,
+            thrust_coefficient_functions=turbine_grid_farm.turbine_thrust_coefficient_functions,
+            tilt_interps=turbine_grid_farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=turbine_grid_farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=turbine_grid_farm.turbine_power_thrust_tables,
             ix_filter=[i],
         )
-        # Since we are filtering for the i'th turbine in the Ct function,
+        # Since we are filtering for the i'th turbine in the thrust_coefficient function,
         # get the first index here (0:1)
         ct_i = ct_i[:, 0:1, None, None]
         axial_induction_i = axial_induction(
             velocities=turbine_grid_flow_field.u_sorted,
-            yaw_angle=turbine_grid_farm.yaw_angles_sorted,
-            tilt_angle=turbine_grid_farm.tilt_angles_sorted,
-            ref_tilt=turbine_grid_farm.ref_tilts_sorted,
-            fCt=turbine_grid_farm.turbine_fCts,
-            tilt_interp=turbine_grid_farm.turbine_tilt_interps,
+            yaw_angles=turbine_grid_farm.yaw_angles_sorted,
+            tilt_angles=turbine_grid_farm.tilt_angles_sorted,
+            axial_induction_functions=turbine_grid_farm.turbine_axial_induction_functions,
+            tilt_interps=turbine_grid_farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=turbine_grid_farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=turbine_grid_farm.turbine_power_thrust_tables,
             ix_filter=[i],
         )
         # Since we are filtering for the i'th turbine in the axial induction function,
@@ -492,15 +487,15 @@ def cc_solver(
         )
 
         turb_avg_vels = average_velocity(turb_inflow_field)
-        turb_Cts = Ct(
+        turb_Cts = thrust_coefficient(
             turb_avg_vels,
             farm.yaw_angles_sorted,
             farm.tilt_angles_sorted,
-            farm.ref_tilts_sorted,
-            farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            farm.turbine_thrust_coefficient_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
         )
@@ -509,11 +504,11 @@ def cc_solver(
             turb_avg_vels,
             farm.yaw_angles_sorted,
             farm.tilt_angles_sorted,
-            farm.ref_tilts_sorted,
-            farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            farm.turbine_axial_induction_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
@@ -525,13 +520,13 @@ def cc_solver(
 
         axial_induction_i = axial_induction(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            axial_induction_functions=farm.turbine_axial_induction_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
@@ -675,14 +670,12 @@ def full_flow_cc_solver(
     turbine_grid_flow_field = copy.deepcopy(flow_field)
 
     turbine_grid_farm.construct_turbine_map()
-    turbine_grid_farm.construct_turbine_fCts()
-    turbine_grid_farm.construct_turbine_power_interps()
+    turbine_grid_farm.construct_turbine_thrust_coefficient_functions()
+    turbine_grid_farm.construct_turbine_axial_induction_functions()
+    turbine_grid_farm.construct_turbine_power_functions()
     turbine_grid_farm.construct_hub_heights()
     turbine_grid_farm.construct_rotor_diameters()
     turbine_grid_farm.construct_turbine_TSRs()
-    turbine_grid_farm.construct_turbine_pPs()
-    turbine_grid_farm.construct_turbine_pTs()
-    turbine_grid_farm.construct_turbine_ref_air_densities()
     turbine_grid_farm.construct_turbine_ref_tilts()
     turbine_grid_farm.construct_turbine_tilt_interps()
     turbine_grid_farm.construct_turbine_correct_cp_ct_for_tilt()
@@ -737,15 +730,15 @@ def full_flow_cc_solver(
         v_i = turbine_grid_flow_field.v_sorted[:, i:i+1]
 
         turb_avg_vels = average_velocity(turbine_grid_flow_field.u_sorted)
-        turb_Cts = Ct(
+        turb_Cts = thrust_coefficient(
             velocities=turb_avg_vels,
-            yaw_angle=turbine_grid_farm.yaw_angles_sorted,
-            tilt_angle=turbine_grid_farm.tilt_angles_sorted,
-            ref_tilt=turbine_grid_farm.ref_tilts_sorted,
-            fCt=turbine_grid_farm.turbine_fCts,
-            tilt_interp=turbine_grid_farm.turbine_tilt_interps,
+            yaw_angles=turbine_grid_farm.yaw_angles_sorted,
+            tilt_angles=turbine_grid_farm.tilt_angles_sorted,
+            thrust_coefficient_functions=turbine_grid_farm.turbine_thrust_coefficient_functions,
+            tilt_interps=turbine_grid_farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=turbine_grid_farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=turbine_grid_farm.turbine_power_thrust_tables,
             average_method=turbine_grid.average_method,
             cubature_weights=turbine_grid.cubature_weights
         )
@@ -753,13 +746,13 @@ def full_flow_cc_solver(
 
         axial_induction_i = axial_induction(
             velocities=turbine_grid_flow_field.u_sorted,
-            yaw_angle=turbine_grid_farm.yaw_angles_sorted,
-            tilt_angle=turbine_grid_farm.tilt_angles_sorted,
-            ref_tilt=turbine_grid_farm.ref_tilts_sorted,
-            fCt=turbine_grid_farm.turbine_fCts,
-            tilt_interp=turbine_grid_farm.turbine_tilt_interps,
+            yaw_angles=turbine_grid_farm.yaw_angles_sorted,
+            tilt_angles=turbine_grid_farm.tilt_angles_sorted,
+            axial_induction_functions=turbine_grid_farm.turbine_axial_induction_functions,
+            tilt_interps=turbine_grid_farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=turbine_grid_farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=turbine_grid_farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=turbine_grid.average_method,
             cubature_weights=turbine_grid.cubature_weights
@@ -888,44 +881,44 @@ def turbopark_solver(
         u_i = flow_field.u_sorted[:, :, i:i+1]
         v_i = flow_field.v_sorted[:, :, i:i+1]
 
-        Cts = Ct(
+        Cts = thrust_coefficient(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            thrust_coefficient_functions=farm.turbine_thrust_coefficient_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
         )
 
-        ct_i = Ct(
+        ct_i = thrust_coefficient(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            thrust_coefficient_functions=farm.turbine_thrust_coefficient_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
         )
-        # Since we are filtering for the i'th turbine in the Ct function,
+        # Since we are filtering for the i'th turbine in the thrust coefficient function,
         # get the first index here (0:1)
         ct_i = ct_i[:, 0:1, None, None]
         axial_induction_i = axial_induction(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            axial_induction_functions=farm.turbine_axial_induction_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
@@ -975,15 +968,15 @@ def turbopark_solver(
 
                 yaw_ii = farm.yaw_angles_sorted[:, ii:ii+1, None, None]
                 turbulence_intensity_ii = turbine_turbulence_intensity[:, ii:ii+1]
-                ct_ii = Ct(
+                ct_ii = thrust_coefficient(
                     velocities=flow_field.u_sorted,
-                    yaw_angle=farm.yaw_angles_sorted,
-                    tilt_angle=farm.tilt_angles_sorted,
-                    ref_tilt=farm.ref_tilts_sorted,
-                    fCt=farm.turbine_fCts,
-                    tilt_interp=farm.turbine_tilt_interps,
+                    yaw_angles=farm.yaw_angles_sorted,
+                    tilt_angles=farm.tilt_angles_sorted,
+                    thrust_coefficient_functions=farm.turbine_thrust_coefficient_functions,
+                    tilt_interps=farm.turbine_tilt_interps,
                     correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
                     turbine_type_map=farm.turbine_type_map_sorted,
+                    turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
                     ix_filter=[ii],
                     average_method=grid.average_method,
                     cubature_weights=grid.cubature_weights
@@ -1170,31 +1163,31 @@ def empirical_gauss_solver(
         flow_field.u_sorted[:, i:i+1]
         flow_field.v_sorted[:, i:i+1]
 
-        ct_i = Ct(
+        ct_i = thrust_coefficient(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            thrust_coefficient_functions=farm.turbine_thrust_coefficient_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
         )
-        # Since we are filtering for the i'th turbine in the Ct function,
+        # Since we are filtering for the i'th turbine in the thrust coefficient function,
         # get the first index here (0:1)
         ct_i = ct_i[:, 0:1, None, None]
         axial_induction_i = axial_induction(
             velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=farm.turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
+            yaw_angles=farm.yaw_angles_sorted,
+            tilt_angles=farm.tilt_angles_sorted,
+            axial_induction_functions=farm.turbine_axial_induction_functions,
+            tilt_interps=farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=farm.turbine_power_thrust_tables,
             ix_filter=[i],
             average_method=grid.average_method,
             cubature_weights=grid.cubature_weights
@@ -1314,14 +1307,12 @@ def full_flow_empirical_gauss_solver(
     turbine_grid_flow_field = copy.deepcopy(flow_field)
 
     turbine_grid_farm.construct_turbine_map()
-    turbine_grid_farm.construct_turbine_fCts()
-    turbine_grid_farm.construct_turbine_power_interps()
+    turbine_grid_farm.construct_turbine_thrust_coefficient_functions()
+    turbine_grid_farm.construct_turbine_axial_induction_functions()
+    turbine_grid_farm.construct_turbine_power_functions()
     turbine_grid_farm.construct_hub_heights()
     turbine_grid_farm.construct_rotor_diameters()
     turbine_grid_farm.construct_turbine_TSRs()
-    turbine_grid_farm.construct_turbine_pPs()
-    turbine_grid_farm.construct_turbine_pTs()
-    turbine_grid_farm.construct_turbine_ref_air_densities()
     turbine_grid_farm.construct_turbine_ref_tilts()
     turbine_grid_farm.construct_turbine_tilt_interps()
     turbine_grid_farm.construct_turbine_correct_cp_ct_for_tilt()
@@ -1373,29 +1364,29 @@ def full_flow_empirical_gauss_solver(
         turbine_grid_flow_field.u_sorted[:, i:i+1]
         turbine_grid_flow_field.v_sorted[:, i:i+1]
 
-        ct_i = Ct(
+        ct_i = thrust_coefficient(
             velocities=turbine_grid_flow_field.u_sorted,
-            yaw_angle=turbine_grid_farm.yaw_angles_sorted,
-            tilt_angle=turbine_grid_farm.tilt_angles_sorted,
-            ref_tilt=turbine_grid_farm.ref_tilts_sorted,
-            fCt=turbine_grid_farm.turbine_fCts,
-            tilt_interp=turbine_grid_farm.turbine_tilt_interps,
+            yaw_angles=turbine_grid_farm.yaw_angles_sorted,
+            tilt_angles=turbine_grid_farm.tilt_angles_sorted,
+            thrust_coefficient_functions=turbine_grid_farm.turbine_thrust_coefficient_functions,
+            tilt_interps=turbine_grid_farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=turbine_grid_farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=turbine_grid_farm.turbine_power_thrust_tables,
             ix_filter=[i],
         )
-        # Since we are filtering for the i'th turbine in the Ct function,
+        # Since we are filtering for the i'th turbine in the thrust coefficient function,
         # get the first index here (0:1)
         ct_i = ct_i[:, 0:1, None, None]
         axial_induction_i = axial_induction(
             velocities=turbine_grid_flow_field.u_sorted,
-            yaw_angle=turbine_grid_farm.yaw_angles_sorted,
-            tilt_angle=turbine_grid_farm.tilt_angles_sorted,
-            ref_tilt=turbine_grid_farm.ref_tilts_sorted,
-            fCt=turbine_grid_farm.turbine_fCts,
-            tilt_interp=turbine_grid_farm.turbine_tilt_interps,
+            yaw_angles=turbine_grid_farm.yaw_angles_sorted,
+            tilt_angles=turbine_grid_farm.tilt_angles_sorted,
+            axial_induction_functions=turbine_grid_farm.turbine_axial_induction_functions,
+            tilt_interps=turbine_grid_farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted,
             turbine_type_map=turbine_grid_farm.turbine_type_map_sorted,
+            turbine_power_thrust_tables=turbine_grid_farm.turbine_power_thrust_tables,
             ix_filter=[i],
         )
         # Since we are filtering for the i'th turbine in the axial induction function,
@@ -1462,207 +1453,3 @@ def full_flow_empirical_gauss_solver(
         flow_field.u_sorted = flow_field.u_initial_sorted - wake_field
         flow_field.v_sorted += v_wake
         flow_field.w_sorted += w_wake
-
-
-def sequential_multidim_solver(
-    farm: Farm,
-    flow_field: FlowField,
-    grid: TurbineGrid,
-    model_manager: WakeModelManager
-) -> None:
-    # Algorithm
-    # For each turbine, calculate its effect on every downstream turbine.
-    # For the current turbine, we are calculating the deficit that it adds to downstream turbines.
-    # Integrate this into the main data structure.
-    # Move on to the next turbine.
-
-    # <<interface>>
-    deflection_model_args = model_manager.deflection_model.prepare_function(grid, flow_field)
-    deficit_model_args = model_manager.velocity_model.prepare_function(grid, flow_field)
-    downselect_turbine_fCts = multidim_Ct_down_select(
-        farm.turbine_fCts_sorted,
-        flow_field.multidim_conditions,
-    )
-
-    # This is u_wake
-    wake_field = np.zeros_like(flow_field.u_initial_sorted)
-    v_wake = np.zeros_like(flow_field.v_initial_sorted)
-    w_wake = np.zeros_like(flow_field.w_initial_sorted)
-
-    turbine_turbulence_intensity = (
-        flow_field.turbulence_intensity * np.ones((flow_field.n_findex, farm.n_turbines, 1, 1))
-    )
-    ambient_turbulence_intensity = flow_field.turbulence_intensity
-
-    # Calculate the velocity deficit sequentially from upstream to downstream turbines
-    for i in range(grid.n_turbines):
-
-        # Get the current turbine quantities
-        x_i = np.mean(grid.x_sorted[:, i:i+1], axis=(2, 3))
-        x_i = x_i[:, :, None, None]
-        y_i = np.mean(grid.y_sorted[:, i:i+1], axis=(2, 3))
-        y_i = y_i[:, :, None, None]
-        z_i = np.mean(grid.z_sorted[:, i:i+1], axis=(2, 3))
-        z_i = z_i[:, :, None, None]
-
-        u_i = flow_field.u_sorted[:, i:i+1]
-        v_i = flow_field.v_sorted[:, i:i+1]
-
-        ct_i = Ct_multidim(
-            velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=downselect_turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
-            correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
-            turbine_type_map=farm.turbine_type_map_sorted,
-            ix_filter=[i],
-            average_method=grid.average_method,
-            cubature_weights=grid.cubature_weights
-        )
-        # Since we are filtering for the i'th turbine in the Ct function,
-        # get the first index here (0:1)
-        ct_i = ct_i[:, 0:1, None, None]
-        axial_induction_i = axial_induction_multidim(
-            velocities=flow_field.u_sorted,
-            yaw_angle=farm.yaw_angles_sorted,
-            tilt_angle=farm.tilt_angles_sorted,
-            ref_tilt=farm.ref_tilts_sorted,
-            fCt=downselect_turbine_fCts,
-            tilt_interp=farm.turbine_tilt_interps,
-            correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted,
-            turbine_type_map=farm.turbine_type_map_sorted,
-            ix_filter=[i],
-            average_method=grid.average_method,
-            cubature_weights=grid.cubature_weights
-        )
-        # Since we are filtering for the i'th turbine in the axial induction function,
-        # get the first index here (0:1)
-        axial_induction_i = axial_induction_i[:, 0:1, None, None]
-        turbulence_intensity_i = turbine_turbulence_intensity[:, i:i+1]
-        yaw_angle_i = farm.yaw_angles_sorted[:, i:i+1, None, None]
-        hub_height_i = farm.hub_heights_sorted[:, i:i+1, None, None]
-        rotor_diameter_i = farm.rotor_diameters_sorted[:, i:i+1, None, None]
-        TSR_i = farm.TSRs_sorted[:, i:i+1, None, None]
-
-        effective_yaw_i = np.zeros_like(yaw_angle_i)
-        effective_yaw_i += yaw_angle_i
-
-        if model_manager.enable_secondary_steering:
-            added_yaw = wake_added_yaw(
-                u_i,
-                v_i,
-                flow_field.u_initial_sorted,
-                grid.y_sorted[:, i:i+1] - y_i,
-                grid.z_sorted[:, i:i+1],
-                rotor_diameter_i,
-                hub_height_i,
-                ct_i,
-                TSR_i,
-                axial_induction_i,
-                flow_field.wind_shear,
-            )
-            effective_yaw_i += added_yaw
-
-        # Model calculations
-        # NOTE: exponential
-        deflection_field = model_manager.deflection_model.function(
-            x_i,
-            y_i,
-            effective_yaw_i,
-            turbulence_intensity_i,
-            ct_i,
-            rotor_diameter_i,
-            **deflection_model_args,
-        )
-
-        if model_manager.enable_transverse_velocities:
-            v_wake, w_wake = calculate_transverse_velocity(
-                u_i,
-                flow_field.u_initial_sorted,
-                flow_field.dudz_initial_sorted,
-                grid.x_sorted - x_i,
-                grid.y_sorted - y_i,
-                grid.z_sorted,
-                rotor_diameter_i,
-                hub_height_i,
-                yaw_angle_i,
-                ct_i,
-                TSR_i,
-                axial_induction_i,
-                flow_field.wind_shear,
-            )
-
-        if model_manager.enable_yaw_added_recovery:
-            I_mixing = yaw_added_turbulence_mixing(
-                u_i,
-                turbulence_intensity_i,
-                v_i,
-                flow_field.w_sorted[:, i:i+1],
-                v_wake[:, i:i+1],
-                w_wake[:, i:i+1],
-            )
-            gch_gain = 2
-            turbine_turbulence_intensity[:, i:i+1] = turbulence_intensity_i + gch_gain * I_mixing
-
-        # NOTE: exponential
-        velocity_deficit = model_manager.velocity_model.function(
-            x_i,
-            y_i,
-            z_i,
-            axial_induction_i,
-            deflection_field,
-            yaw_angle_i,
-            turbulence_intensity_i,
-            ct_i,
-            hub_height_i,
-            rotor_diameter_i,
-            **deficit_model_args,
-        )
-
-        wake_field = model_manager.combination_model.function(
-            wake_field,
-            velocity_deficit * flow_field.u_initial_sorted
-        )
-
-        wake_added_turbulence_intensity = model_manager.turbulence_model.function(
-            ambient_turbulence_intensity,
-            grid.x_sorted,
-            x_i,
-            rotor_diameter_i,
-            axial_induction_i,
-        )
-
-        # Calculate wake overlap for wake-added turbulence (WAT)
-        area_overlap = (
-            np.sum(velocity_deficit * flow_field.u_initial_sorted > 0.05, axis=(2, 3))
-            / (grid.grid_resolution * grid.grid_resolution)
-        )
-        area_overlap = area_overlap[:, :, None, None]
-
-        # Modify wake added turbulence by wake area overlap
-        downstream_influence_length = 15 * rotor_diameter_i
-        ti_added = (
-            area_overlap
-            * np.nan_to_num(wake_added_turbulence_intensity, posinf=0.0)
-            * (grid.x_sorted > x_i)
-            * (np.abs(y_i - grid.y_sorted) < 2 * rotor_diameter_i)
-            * (grid.x_sorted <= downstream_influence_length + x_i)
-        )
-
-        # Combine turbine TIs with WAT
-        turbine_turbulence_intensity = np.maximum(
-            np.sqrt( ti_added ** 2 + ambient_turbulence_intensity ** 2 ),
-            turbine_turbulence_intensity
-        )
-
-        flow_field.u_sorted = flow_field.u_initial_sorted - wake_field
-        flow_field.v_sorted += v_wake
-        flow_field.w_sorted += w_wake
-
-    flow_field.turbulence_intensity_field_sorted = turbine_turbulence_intensity
-    flow_field.turbulence_intensity_field_sorted_avg = np.mean(
-        turbine_turbulence_intensity,
-        axis=(2,3)
-    )[:, :, None, None]
diff --git a/floris/simulation/turbine.py b/floris/simulation/turbine.py
deleted file mode 100644
index d7306ada5..000000000
--- a/floris/simulation/turbine.py
+++ /dev/null
@@ -1,684 +0,0 @@
-# Copyright 2021 NREL
-
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not
-# use this file except in compliance with the License. You may obtain a copy of
-# the License at http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
-# License for the specific language governing permissions and limitations under
-# the License.
-
-# See https://floris.readthedocs.io for documentation
-
-from __future__ import annotations
-
-import copy
-from collections.abc import Iterable
-
-import attrs
-import numpy as np
-from attrs import define, field
-from scipy.interpolate import interp1d
-
-from floris.simulation import BaseClass
-from floris.type_dec import (
-    floris_numeric_dict_converter,
-    NDArrayBool,
-    NDArrayFilter,
-    NDArrayFloat,
-    NDArrayInt,
-    NDArrayObject,
-)
-from floris.utilities import cosd
-
-
-def _rotor_velocity_yaw_correction(
-    pP: float,
-    yaw_angle: NDArrayFloat,
-    rotor_effective_velocities: NDArrayFloat,
-) -> NDArrayFloat:
-    # Compute the rotor effective velocity adjusting for yaw settings
-    pW = pP / 3.0  # Convert from pP to w
-    rotor_effective_velocities = rotor_effective_velocities * cosd(yaw_angle) ** pW
-
-    return rotor_effective_velocities
-
-
-def _rotor_velocity_tilt_correction(
-    turbine_type_map: NDArrayObject,
-    tilt_angle: NDArrayFloat,
-    ref_tilt: NDArrayFloat,
-    pT: float,
-    tilt_interp: NDArrayObject,
-    correct_cp_ct_for_tilt: NDArrayBool,
-    rotor_effective_velocities: NDArrayFloat,
-) -> NDArrayFloat:
-    # Compute the tilt, if using floating turbines
-    old_tilt_angle = copy.deepcopy(tilt_angle)
-    tilt_angle = compute_tilt_angles_for_floating_turbines(
-        turbine_type_map,
-        tilt_angle,
-        tilt_interp,
-        rotor_effective_velocities,
-    )
-    # Only update tilt angle if requested (if the tilt isn't accounted for in the Cp curve)
-    tilt_angle = np.where(correct_cp_ct_for_tilt, tilt_angle, old_tilt_angle)
-
-    # Compute the rotor effective velocity adjusting for tilt
-    relative_tilt = tilt_angle - ref_tilt
-    rotor_effective_velocities = rotor_effective_velocities * cosd(relative_tilt) ** (pT / 3.0)
-    return rotor_effective_velocities
-
-
-def compute_tilt_angles_for_floating_turbines(
-    turbine_type_map: NDArrayObject,
-    tilt_angle: NDArrayFloat,
-    tilt_interp: dict[str, interp1d],
-    rotor_effective_velocities: NDArrayFloat,
-) -> NDArrayFloat:
-    # Loop over each turbine type given to get tilt angles for all turbines
-    tilt_angles = np.zeros(np.shape(rotor_effective_velocities))
-    turb_types = np.unique(turbine_type_map)
-    for turb_type in turb_types:
-        # If no tilt interpolation is specified, assume no modification to tilt
-        if tilt_interp[turb_type] is None:
-            # TODO should this be break? Should it be continue? Do we want to support mixed
-            # fixed-bottom and floating? Or non-tilting floating?
-            pass
-        # Using a masked array, apply the tilt angle for all turbines of the current
-        # type to the main tilt angle array
-        else:
-            tilt_angles += (
-                tilt_interp[turb_type](rotor_effective_velocities)
-                * (turbine_type_map == turb_type)
-            )
-
-    # TODO: Not sure if this is the best way to do this? Basically replaces the initialized
-    # tilt_angles if there are non-zero tilt angles calculated above (meaning that the turbine
-    # definition contained  a wind_speed/tilt table definition)
-    if not tilt_angles.all() == 0.0:
-        tilt_angle = tilt_angles
-
-    return tilt_angle
-
-
-def rotor_effective_velocity(
-    air_density: float,
-    ref_air_density: float,
-    velocities: NDArrayFloat,
-    yaw_angle: NDArrayFloat,
-    tilt_angle: NDArrayFloat,
-    ref_tilt: NDArrayFloat,
-    pP: float,
-    pT: float,
-    tilt_interp: NDArrayObject,
-    correct_cp_ct_for_tilt: NDArrayBool,
-    turbine_type_map: NDArrayObject,
-    ix_filter: NDArrayInt | Iterable[int] | None = None,
-    average_method: str = "cubic-mean",
-    cubature_weights: NDArrayFloat | None = None
-) -> NDArrayFloat:
-
-    if isinstance(yaw_angle, list):
-        yaw_angle = np.array(yaw_angle)
-    if isinstance(tilt_angle, list):
-        tilt_angle = np.array(tilt_angle)
-
-    # Down-select inputs if ix_filter is given
-    if ix_filter is not None:
-        velocities = velocities[:, ix_filter]
-        yaw_angle = yaw_angle[:, ix_filter]
-        tilt_angle = tilt_angle[:, ix_filter]
-        ref_tilt = ref_tilt[:, ix_filter]
-        pP = pP[:, ix_filter]
-        pT = pT[:, ix_filter]
-        turbine_type_map = turbine_type_map[:, ix_filter]
-
-    # Compute the rotor effective velocity adjusting for air density
-    # TODO: This correction is currently split across two functions: this one and `power`, where in
-    # `power` the returned power is multiplied by the reference air density
-    average_velocities = average_velocity(
-        velocities,
-        method=average_method,
-        cubature_weights=cubature_weights
-    )
-    rotor_effective_velocities = (air_density/ref_air_density)**(1/3) * average_velocities
-
-    # Compute the rotor effective velocity adjusting for yaw settings
-    rotor_effective_velocities = _rotor_velocity_yaw_correction(
-        pP, yaw_angle, rotor_effective_velocities
-    )
-
-    # Compute the tilt, if using floating turbines
-    rotor_effective_velocities = _rotor_velocity_tilt_correction(
-        turbine_type_map,
-        tilt_angle,
-        ref_tilt,
-        pT,
-        tilt_interp,
-        correct_cp_ct_for_tilt,
-        rotor_effective_velocities,
-    )
-
-    return rotor_effective_velocities
-
-
-def power(
-    rotor_effective_velocities: NDArrayFloat,
-    power_interp: dict[str, interp1d],
-    turbine_type_map: NDArrayObject,
-    ix_filter: NDArrayInt | Iterable[int] | None = None,
-) -> NDArrayFloat:
-    """Power produced by a turbine adjusted for yaw and tilt. Value
-    given in Watts.
-
-    Args:
-        rotor_effective_velocities (NDArrayFloat[wd, ws, turbines]): The rotor
-            effective velocities at a turbine. Includes the air density correction.
-        power_interp (dict[str, interp1d]): A dictionary of power interpolation functions for
-            each turbine type.
-        turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition for
-            each turbine.
-        ix_filter (NDArrayInt, optional): The boolean array, or
-            integer indices to filter out before calculation. Defaults to None.
-
-    Returns:
-        NDArrayFloat: The power, in Watts, for each turbine after adjusting for yaw and tilt.
-    """
-    # TODO: Change the order of input arguments to be consistent with the other
-    # utility functions - velocities first...
-    # Update to power calculation which replaces the fixed pP exponent with
-    # an exponent pW, that changes the effective wind speed input to the power
-    # calculation, rather than scaling the power.  This better handles power
-    # loss to yaw in above rated conditions
-    #
-    # based on the paper "Optimising yaw control at wind farm level" by
-    # Ervin Bossanyi
-
-    # TODO: check this - where is it?
-    # P = 1/2 rho A V^3 Cp
-
-    # Down-select inputs if ix_filter is given
-    if ix_filter is not None:
-        rotor_effective_velocities = rotor_effective_velocities[:, ix_filter]
-        turbine_type_map = turbine_type_map[:, ix_filter]
-
-    # Loop over each turbine type given to get power for all turbines
-    p = np.zeros(np.shape(rotor_effective_velocities))
-    turb_types = np.unique(turbine_type_map)
-    for turb_type in turb_types:
-        # Using a masked array, apply the thrust coefficient for all turbines of the current
-        # type to the main thrust coefficient array
-        p += power_interp[turb_type](rotor_effective_velocities) * (turbine_type_map == turb_type)
-
-    return p
-
-
-def Ct(
-    velocities: NDArrayFloat,
-    yaw_angle: NDArrayFloat,
-    tilt_angle: NDArrayFloat,
-    ref_tilt: NDArrayFloat,
-    fCt: dict,
-    tilt_interp: NDArrayObject,
-    correct_cp_ct_for_tilt: NDArrayBool,
-    turbine_type_map: NDArrayObject,
-    ix_filter: NDArrayFilter | Iterable[int] | None = None,
-    average_method: str = "cubic-mean",
-    cubature_weights: NDArrayFloat | None = None
-) -> NDArrayFloat:
-
-    """Thrust coefficient of a turbine incorporating the yaw angle.
-    The value is interpolated from the coefficient of thrust vs
-    wind speed table using the rotor swept area average velocity.
-
-    Args:
-        velocities (NDArrayFloat[findex, turbines, grid1, grid2]): The velocity field at
-            a turbine.
-        yaw_angle (NDArrayFloat[findex, turbines]): The yaw angle for each turbine.
-        tilt_angle (NDArrayFloat[findex, turbines]): The tilt angle for each turbine.
-        ref_tilt (NDArrayFloat[findex, turbines]): The reference tilt angle for each turbine
-            that the Cp/Ct tables are defined at.
-        fCt (dict): The thrust coefficient interpolation functions for each turbine. Keys are
-            the turbine type string and values are the interpolation functions.
-        tilt_interp (Iterable[tuple]): The tilt interpolation functions for each
-            turbine.
-        correct_cp_ct_for_tilt (NDArrayBool[findex, turbines]): Boolean for determining if the
-            turbines Cp and Ct should be corrected for tilt.
-        turbine_type_map: (NDArrayObject[findex, turbines]): The Turbine type definition
-            for each turbine.
-        ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or
-            integer indices as an iterable of array to filter out before calculation.
-            Defaults to None.
-
-    Returns:
-        NDArrayFloat: Coefficient of thrust for each requested turbine.
-    """
-
-    if isinstance(yaw_angle, list):
-        yaw_angle = np.array(yaw_angle)
-
-    if isinstance(tilt_angle, list):
-        tilt_angle = np.array(tilt_angle)
-
-    # Down-select inputs if ix_filter is given
-    if ix_filter is not None:
-        velocities = velocities[:, ix_filter]
-        yaw_angle = yaw_angle[:, ix_filter]
-        tilt_angle = tilt_angle[:, ix_filter]
-        ref_tilt = ref_tilt[:, ix_filter]
-        turbine_type_map = turbine_type_map[:, ix_filter]
-        correct_cp_ct_for_tilt = correct_cp_ct_for_tilt[:, ix_filter]
-
-    average_velocities = average_velocity(
-        velocities,
-        method=average_method,
-        cubature_weights=cubature_weights
-    )
-
-    # Compute the tilt, if using floating turbines
-    old_tilt_angle = copy.deepcopy(tilt_angle)
-    tilt_angle = compute_tilt_angles_for_floating_turbines(
-        turbine_type_map,
-        tilt_angle,
-        tilt_interp,
-        average_velocities,
-    )
-    # Only update tilt angle if requested (if the tilt isn't accounted for in the Ct curve)
-    tilt_angle = np.where(correct_cp_ct_for_tilt, tilt_angle, old_tilt_angle)
-
-    # Loop over each turbine type given to get thrust coefficient for all turbines
-    thrust_coefficient = np.zeros(np.shape(average_velocities))
-    turb_types = np.unique(turbine_type_map)
-    for turb_type in turb_types:
-        # Using a masked array, apply the thrust coefficient for all turbines of the current
-        # type to the main thrust coefficient array
-        thrust_coefficient += (
-            fCt[turb_type](average_velocities)
-            * (turbine_type_map == turb_type)
-        )
-    thrust_coefficient = np.clip(thrust_coefficient, 0.0001, 0.9999)
-    effective_thrust = thrust_coefficient * cosd(yaw_angle) * cosd(tilt_angle - ref_tilt)
-    return effective_thrust
-
-
-def axial_induction(
-    velocities: NDArrayFloat,  # (findex, turbines, grid, grid)
-    yaw_angle: NDArrayFloat,  # (findex, turbines)
-    tilt_angle: NDArrayFloat,  # (findex, turbines)
-    ref_tilt: NDArrayFloat,
-    fCt: dict,  # (turbines)
-    tilt_interp: NDArrayObject,  # (turbines)
-    correct_cp_ct_for_tilt: NDArrayBool, # (findex, turbines)
-    turbine_type_map: NDArrayObject, # (findex, turbines)
-    ix_filter: NDArrayFilter | Iterable[int] | None = None,
-    average_method: str = "cubic-mean",
-    cubature_weights: NDArrayFloat | None = None
-) -> NDArrayFloat:
-    """Axial induction factor of the turbine incorporating
-    the thrust coefficient and yaw angle.
-
-    Args:
-        velocities (NDArrayFloat): The velocity field at each turbine; should be shape:
-            (number of turbines, ngrid, ngrid), or (ngrid, ngrid) for a single turbine.
-        yaw_angle (NDArrayFloat[findex, turbines]): The yaw angle for each turbine.
-        tilt_angle (NDArrayFloat[findex, turbines]): The tilt angle for each turbine.
-        ref_tilt (NDArrayFloat[findex, turbines]): The reference tilt angle for each turbine
-            that the Cp/Ct tables are defined at.
-        fCt (dict): The thrust coefficient interpolation functions for each turbine. Keys are
-            the turbine type string and values are the interpolation functions.
-        tilt_interp (Iterable[tuple]): The tilt interpolation functions for each
-            turbine.
-        correct_cp_ct_for_tilt (NDArrayBool[findex, turbines]): Boolean for determining if the
-            turbines Cp and Ct should be corrected for tilt.
-        turbine_type_map: (NDArrayObject[findex, turbines]): The Turbine type definition
-            for each turbine.
-        ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or
-            integer indices (as an array or iterable) to filter out before calculation.
-            Defaults to None.
-
-    Returns:
-        Union[float, NDArrayFloat]: [description]
-    """
-
-    if isinstance(yaw_angle, list):
-        yaw_angle = np.array(yaw_angle)
-
-    # TODO: Should the tilt_angle used for the return calculation be modified the same as the
-    # tilt_angle in Ct, if the user has supplied a tilt/wind_speed table?
-    if isinstance(tilt_angle, list):
-        tilt_angle = np.array(tilt_angle)
-
-    # Get Ct first before modifying any data
-    thrust_coefficient = Ct(
-        velocities,
-        yaw_angle,
-        tilt_angle,
-        ref_tilt,
-        fCt,
-        tilt_interp,
-        correct_cp_ct_for_tilt,
-        turbine_type_map,
-        ix_filter,
-        average_method,
-        cubature_weights
-    )
-
-    # Then, process the input arguments as needed for this function
-    if ix_filter is not None:
-        yaw_angle = yaw_angle[:, ix_filter]
-        tilt_angle = tilt_angle[:, ix_filter]
-        ref_tilt = ref_tilt[:, ix_filter]
-
-    return (
-        0.5
-        / (cosd(yaw_angle)
-        * cosd(tilt_angle - ref_tilt))
-        * (
-            1 - np.sqrt(
-                1 - thrust_coefficient * cosd(yaw_angle) * cosd(tilt_angle - ref_tilt)
-            )
-        )
-    )
-
-
-def simple_mean(array, axis=0):
-    return np.mean(array, axis=axis)
-
-def cubic_mean(array, axis=0):
-    return np.cbrt(np.mean(array ** 3.0, axis=axis))
-
-def simple_cubature(array, cubature_weights, axis=0):
-    weights = cubature_weights.flatten()
-    weights = weights * len(weights) / np.sum(weights)
-    product = (array * weights[None, None, :, None])
-    return simple_mean(product, axis)
-
-def cubic_cubature(array, cubature_weights, axis=0):
-    weights = cubature_weights.flatten()
-    weights = weights * len(weights) / np.sum(weights)
-    return np.cbrt(np.mean((array**3.0 * weights[None, None, :, None]), axis=axis))
-
-def average_velocity(
-    velocities: NDArrayFloat,
-    ix_filter: NDArrayFilter | Iterable[int] | None = None,
-    method: str = "cubic-mean",
-    cubature_weights: NDArrayFloat | None = None
-) -> NDArrayFloat:
-    """This property calculates and returns the average of the velocity field
-    in turbine's rotor swept area. The average is calculated using the
-    user-specified method. This is a vectorized function, so it can be used
-    to calculate the average velocity for multiple turbines at once or
-    a single turbine.
-
-    **Note:** The velocity is scaled to an effective velocity by the yaw.
-
-    Args:
-        velocities (NDArrayFloat): The velocity field at each turbine; should be shape:
-            (number of turbines, ngrid, ngrid), or (ngrid, ngrid) for a single turbine.
-        ix_filter (NDArrayFilter | Iterable[int] | None], optional): The boolean array, or
-            integer indices (as an iterable or array) to filter out before calculation.
-            Defaults to None.
-        method (str, optional): The method to use for averaging. Options are:
-            - "simple-mean": The simple mean of the velocities
-            - "cubic-mean": The cubic mean of the velocities
-            - "simple-cubature": A cubature integration of the velocities
-            - "cubic-cubature": A cubature integration of the cube of the velocities
-            Defaults to "cubic-mean".
-        cubature_weights (NDArrayFloat, optional): The cubature weights to use for the
-            cubature integration methods. Defaults to None.
-
-    Returns:
-        NDArrayFloat: The average velocity across the rotor(s).
-    """
-
-    # The input velocities are expected to be a 5 dimensional array with shape:
-    # (# findex, # turbines, grid resolution, grid resolution)
-
-    if ix_filter is not None:
-        velocities = velocities[:, ix_filter]
-
-    axis = tuple([2 + i for i in range(velocities.ndim - 2)])
-    if method == "simple-mean":
-        return simple_mean(velocities, axis)
-
-    elif method == "cubic-mean":
-        return cubic_mean(velocities, axis)
-
-    elif method == "simple-cubature":
-        if cubature_weights is None:
-            raise ValueError("cubature_weights is required for 'simple-cubature' method.")
-        return simple_cubature(velocities, cubature_weights, axis)
-
-    elif method == "cubic-cubature":
-        if cubature_weights is None:
-            raise ValueError("cubature_weights is required for 'cubic-cubature' method.")
-        return cubic_cubature(velocities, cubature_weights, axis)
-
-    else:
-        raise ValueError("Incorrect method given.")
-
-@define
-class Turbine(BaseClass):
-    """
-    A class containing the parameters and infrastructure to model a wind turbine's performance
-    for a particular atmospheric condition.
-
-    Args:
-        turbine_type (str): An identifier for this type of turbine such as "NREL_5MW".
-        rotor_diameter (float): The rotor diameter in meters.
-        hub_height (float): The hub height in meters.
-        pP (float): The cosine exponent relating the yaw misalignment angle to turbine power.
-        pT (float): The cosine exponent relating the rotor tilt angle to turbine power.
-        TSR (float): The Tip Speed Ratio of the turbine.
-        generator_efficiency (float): The efficiency of the generator used to scale
-            power production.
-        ref_air_density (float): The density at which the provided Cp and Ct curves are defined.
-        ref_tilt (float): The implicit tilt of the turbine for which the Cp and Ct
-            curves are defined. This is typically the nacelle tilt.
-        power_thrust_table (dict[str, float]): Contains power coefficient and thrust coefficient
-            values at a series of wind speeds to define the turbine performance.
-            The dictionary must have the following three keys with equal length values:
-                {
-                    "wind_speeds": List[float],
-                    "power": List[float],
-                    "thrust": List[float],
-                }
-        correct_cp_ct_for_tilt (bool): A flag to indicate whether to correct Cp and Ct for tilt
-            usually for a floating turbine.
-            Optional, defaults to False.
-        floating_tilt_table (dict[str, float]): Look up table of tilt angles at a series of
-            wind speeds. The dictionary must have the following keys with equal length values:
-                {
-                    "wind_speeds": List[float],
-                    "tilt": List[float],
-                }
-            Required if `correct_cp_ct_for_tilt = True`. Defaults to None.
-    """
-    turbine_type: str = field()
-    rotor_diameter: float = field()
-    hub_height: float = field()
-    pP: float = field()
-    pT: float = field()
-    TSR: float = field()
-    generator_efficiency: float = field()
-    ref_air_density: float = field()
-    ref_tilt: float = field()
-    power_thrust_table: dict[str, NDArrayFloat] = field(converter=floris_numeric_dict_converter)
-
-    correct_cp_ct_for_tilt: bool = field(default=False)
-    floating_tilt_table: dict[str, NDArrayFloat] | None = field(default=None)
-
-    # Even though this Turbine class does not support the multidimensional features as they
-    # are implemented in TurbineMultiDim, providing the following two attributes here allows
-    # the turbine data inputs to keep the multidimensional Cp and Ct curve but switch them off
-    # with multi_dimensional_cp_ct = False
-    multi_dimensional_cp_ct: bool = field(default=False)
-    power_thrust_data_file: str = field(default=None)
-
-    # Initialized in the post_init function
-    rotor_radius: float = field(init=False)
-    rotor_area: float = field(init=False)
-    fCt_interp: interp1d = field(init=False)
-    power_interp: interp1d = field(init=False)
-    tilt_interp: interp1d = field(init=False, default=None)
-
-    def __attrs_post_init__(self) -> None:
-        self._initialize_power_thrust_interpolation()
-        self.__post_init__()
-
-    def __post_init__(self) -> None:
-        self._initialize_tilt_interpolation()
-
-    def _initialize_power_thrust_interpolation(self) -> None:
-        # TODO This validation for the power thrust tables should go in the turbine library
-        # since it's preprocessing
-        # Remove any duplicate wind speed entries
-        # _, duplicate_filter = np.unique(self.wind_speed, return_index=True)
-        # self.power = self.power[duplicate_filter]
-        # self.thrust = self.thrust[duplicate_filter]
-        # self.wind_speed = self.wind_speed[duplicate_filter]
-
-        wind_speeds = self.power_thrust_table["wind_speed"]
-        self.power_interp = interp1d(
-            wind_speeds,
-            self.power_thrust_table["power"] * 1e3, # Convert to W
-            fill_value=0.0,
-            bounds_error=False,
-        )
-
-        """
-        Given an array of wind speeds, this function returns an array of the
-        interpolated thrust coefficients from the power / thrust table used
-        to define the Turbine. The values are bound by the range of the input
-        values. Any requested wind speeds outside of the range of input wind
-        speeds are assigned Ct of 0.0001 or 0.9999.
-
-        The fill_value arguments sets (upper, lower) bounds for any values
-        outside of the input range.
-        """
-        self.fCt_interp = interp1d(
-            wind_speeds,
-            self.power_thrust_table["thrust_coefficient"],
-            fill_value=(0.0001, 0.9999),
-            bounds_error=False,
-        )
-
-    def _initialize_tilt_interpolation(self) -> None:
-        # TODO:
-        # Remove any duplicate wind speed entries
-        # _, duplicate_filter = np.unique(self.wind_speeds, return_index=True)
-        # self.tilt = self.tilt[duplicate_filter]
-        # self.wind_speeds = self.wind_speeds[duplicate_filter]
-
-        if self.floating_tilt_table is not None:
-            self.floating_tilt_table = floris_numeric_dict_converter(self.floating_tilt_table)
-
-        # If defined, create a tilt interpolation function for floating turbines.
-        # fill_value currently set to apply the min or max tilt angles if outside
-        # of the interpolation range.
-        if self.correct_cp_ct_for_tilt:
-            self.tilt_interp = interp1d(
-                self.floating_tilt_table["wind_speed"],
-                self.floating_tilt_table["tilt"],
-                fill_value=(0.0, self.floating_tilt_table["tilt"][-1]),
-                bounds_error=False,
-            )
-
-    @power_thrust_table.validator
-    def check_power_thrust_table(self, instance: attrs.Attribute, value: dict) -> None:
-        """
-        Verify that the power and thrust tables are given with arrays of equal length
-        to the wind speed array.
-        """
-        if (len(value.keys()) != 3 or
-            set(value.keys()) != {"wind_speed", "power", "thrust_coefficient"}):
-            raise ValueError(
-                """
-                power_thrust_table dictionary must have the form:
-                    {
-                        "wind_speed": List[float],
-                        "power": List[float],
-                        "thrust_coefficient": List[float],
-                    }
-                """
-            )
-
-        if any(e.ndim > 1 for e in
-            (value["power"], value["thrust_coefficient"], value["wind_speed"])
-            ):
-            raise ValueError("power, thrust_coefficient, and wind_speed inputs must be 1-D.")
-
-        if (len( {value["power"].size, value["thrust_coefficient"].size, value["wind_speed"].size} )
-            > 1):
-            raise ValueError(
-                "power, thrust_coefficient, and wind_speed tables must be the same size."
-            )
-
-    @rotor_diameter.validator
-    def reset_rotor_diameter_dependencies(self, instance: attrs.Attribute, value: float) -> None:
-        """Resets the `rotor_radius` and `rotor_area` attributes."""
-        # Temporarily turn off validators to avoid infinite recursion
-        with attrs.validators.disabled():
-            # Reset the values
-            self.rotor_radius = value / 2.0
-            self.rotor_area = np.pi * self.rotor_radius ** 2.0
-
-    @rotor_radius.validator
-    def reset_rotor_radius(self, instance: attrs.Attribute, value: float) -> None:
-        """
-        Resets the `rotor_diameter` value to trigger the recalculation of
-        `rotor_diameter`, `rotor_radius` and `rotor_area`.
-        """
-        self.rotor_diameter = value * 2.0
-
-    @rotor_area.validator
-    def reset_rotor_area(self, instance: attrs.Attribute, value: float) -> None:
-        """
-        Resets the `rotor_radius` value to trigger the recalculation of
-        `rotor_diameter`, `rotor_radius` and `rotor_area`.
-        """
-        self.rotor_radius = (value / np.pi) ** 0.5
-
-    @floating_tilt_table.validator
-    def check_floating_tilt_table(self, instance: attrs.Attribute, value: dict | None) -> None:
-        """
-        If the tilt / wind_speed table is defined, verify that the tilt and
-        wind_speed arrays are the same length.
-        """
-        if value is None:
-            return
-
-        if len(value.keys()) != 2 or set(value.keys()) != {"wind_speed", "tilt"}:
-            raise ValueError(
-                """
-                floating_tilt_table dictionary must have the form:
-                    {
-                        "wind_speed": List[float],
-                        "tilt": List[float],
-                    }
-                """
-            )
-
-        if any(len(np.shape(e)) > 1 for e in (value["tilt"], value["wind_speed"])):
-            raise ValueError("tilt and wind_speed inputs must be 1-D.")
-
-        if len( {len(value["tilt"]), len(value["wind_speed"])} ) > 1:
-            raise ValueError("tilt and wind_speed inputs must be the same size.")
-
-    @correct_cp_ct_for_tilt.validator
-    def check_for_cp_ct_correct_flag_if_floating(
-        self,
-        instance: attrs.Attribute,
-        value: bool
-    ) -> None:
-        """
-        Check that the boolean flag exists for correcting Cp/Ct for tilt
-        if a tile/wind_speed table is also defined.
-        """
-        if self.correct_cp_ct_for_tilt and self.floating_tilt_table is None:
-            raise ValueError(
-                "To enable the Cp and Ct tilt correction, a tilt table must be given."
-            )
diff --git a/floris/simulation/turbine/__init__.py b/floris/simulation/turbine/__init__.py
new file mode 100644
index 000000000..f1ccca6d0
--- /dev/null
+++ b/floris/simulation/turbine/__init__.py
@@ -0,0 +1,18 @@
+# Copyright 2021 NREL
+
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+# See https://floris.readthedocs.io for documentation
+
+from floris.simulation.turbine.operation_models import (
+    CosineLossTurbine,
+    SimpleTurbine,
+)
diff --git a/floris/simulation/turbine/operation_models.py b/floris/simulation/turbine/operation_models.py
new file mode 100644
index 000000000..93173f364
--- /dev/null
+++ b/floris/simulation/turbine/operation_models.py
@@ -0,0 +1,317 @@
+# Copyright 2021 NREL
+
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+# See https://floris.readthedocs.io for documentation
+
+from __future__ import annotations
+
+import copy
+from abc import abstractmethod
+from typing import (
+    Any,
+    Dict,
+    Final,
+)
+
+import numpy as np
+from attrs import define, field
+from scipy.interpolate import interp1d
+
+from floris.simulation import BaseClass
+from floris.simulation.rotor_velocity import (
+    average_velocity,
+    compute_tilt_angles_for_floating_turbines,
+    rotor_velocity_tilt_correction,
+    rotor_velocity_yaw_correction,
+)
+from floris.type_dec import (
+    NDArrayFloat,
+    NDArrayObject,
+)
+from floris.utilities import cosd
+
+
+def rotor_velocity_air_density_correction(
+    velocities: NDArrayFloat,
+    air_density: float,
+    ref_air_density: float,
+) -> NDArrayFloat:
+    # Produce equivalent velocities at the reference air density
+    # TODO: This could go on BaseTurbineModel
+    return (air_density/ref_air_density)**(1/3) * velocities
+
+
+@define
+class BaseOperationModel(BaseClass):
+    """
+    Base class for turbine operation models. All turbine operation models must implement static
+    power(), thrust_coefficient(), and axial_induction() methods, which are called by power() and
+    thrust_coefficient() through the interface in the turbine.py module.
+
+    Args:
+        BaseClass (_type_): _description_
+
+    Raises:
+        NotImplementedError: _description_
+        NotImplementedError: _description_
+    """
+    @staticmethod
+    @abstractmethod
+    def power() -> None:
+        raise NotImplementedError("BaseOperationModel.power")
+
+    @staticmethod
+    @abstractmethod
+    def thrust_coefficient() -> None:
+        raise NotImplementedError("BaseOperationModel.thrust_coefficient")
+
+    @staticmethod
+    @abstractmethod
+    def axial_induction() -> None:
+        raise NotImplementedError("BaseOperationModel.axial_induction")
+
+@define
+class SimpleTurbine(BaseOperationModel):
+    """
+    Static class defining an actuator disk turbine model that is fully aligned with the flow. No
+    handling for yaw or tilt angles.
+
+    As with all turbine submodules, implements only static power() and thrust_coefficient() methods,
+    which are called by power() and thrust_coefficient() on turbine.py, respectively. This class is
+    not intended to be instantiated; it simply defines a library of static methods.
+
+    TODO: Should the turbine submodels each implement axial_induction()?
+    """
+
+    def power(
+        power_thrust_table: dict,
+        velocities: NDArrayFloat,
+        air_density: float,
+        average_method: str = "cubic-mean",
+        cubature_weights: NDArrayFloat | None = None,
+        **_ # <- Allows other models to accept other keyword arguments
+    ):
+        # Construct power interpolant
+        power_interpolator = interp1d(
+            power_thrust_table["wind_speed"],
+            power_thrust_table["power"],
+            fill_value=0.0,
+            bounds_error=False,
+        )
+
+        # Compute the power-effective wind speed across the rotor
+        rotor_average_velocities = average_velocity(
+            velocities=velocities,
+            method=average_method,
+            cubature_weights=cubature_weights,
+        )
+
+        rotor_effective_velocities = rotor_velocity_air_density_correction(
+            velocities=rotor_average_velocities,
+            air_density=air_density,
+            ref_air_density=power_thrust_table["ref_air_density"]
+        )
+
+        # Compute power
+        power = power_interpolator(rotor_effective_velocities) * 1e3 # Convert to W
+
+        return power
+
+    def thrust_coefficient(
+        power_thrust_table: dict,
+        velocities: NDArrayFloat,
+        average_method: str = "cubic-mean",
+        cubature_weights: NDArrayFloat | None = None,
+        **_ # <- Allows other models to accept other keyword arguments
+    ):
+        # Construct thrust coefficient interpolant
+        thrust_coefficient_interpolator = interp1d(
+            power_thrust_table["wind_speed"],
+            power_thrust_table["thrust_coefficient"],
+            fill_value=0.0001,
+            bounds_error=False,
+        )
+
+        # Compute the effective wind speed across the rotor
+        rotor_average_velocities = average_velocity(
+            velocities=velocities,
+            method=average_method,
+            cubature_weights=cubature_weights,
+        )
+
+        # TODO: Do we need an air density correction here?
+
+        thrust_coefficient = thrust_coefficient_interpolator(rotor_average_velocities)
+        thrust_coefficient = np.clip(thrust_coefficient, 0.0001, 0.9999)
+
+        return thrust_coefficient
+
+    def axial_induction(
+        power_thrust_table: dict,
+        velocities: NDArrayFloat,
+        average_method: str = "cubic-mean",
+        cubature_weights: NDArrayFloat | None = None,
+        **_ # <- Allows other models to accept other keyword arguments
+    ):
+
+        thrust_coefficient = SimpleTurbine.thrust_coefficient(
+            power_thrust_table=power_thrust_table,
+            velocities=velocities,
+            average_method=average_method,
+            cubature_weights=cubature_weights,
+        )
+
+        return (1 - np.sqrt(1 - thrust_coefficient))/2
+
+
+@define
+class CosineLossTurbine(BaseOperationModel):
+    """
+    Static class defining an actuator disk turbine model that may be misaligned with the flow.
+    Nonzero tilt and yaw angles are handled via cosine relationships, with the power lost to yawing
+    defined by the pP exponent. This turbine submodel is the default, and matches the turbine
+    model in FLORIS v3.
+
+    As with all turbine submodules, implements only static power() and thrust_coefficient() methods,
+    which are called by power() and thrust_coefficient() on turbine.py, respectively. This class is
+    not intended to be instantiated; it simply defines a library of static methods.
+
+    TODO: Should the turbine submodels each implement axial_induction()?
+    """
+
+    def power(
+        power_thrust_table: dict,
+        velocities: NDArrayFloat,
+        air_density: float,
+        yaw_angles: NDArrayFloat,
+        tilt_angles: NDArrayFloat,
+        tilt_interp: NDArrayObject,
+        average_method: str = "cubic-mean",
+        cubature_weights: NDArrayFloat | None = None,
+        correct_cp_ct_for_tilt: bool = False,
+        **_ # <- Allows other models to accept other keyword arguments
+    ):
+        # Construct power interpolant
+        power_interpolator = interp1d(
+            power_thrust_table["wind_speed"],
+            power_thrust_table["power"],
+            fill_value=0.0,
+            bounds_error=False,
+        )
+
+        # Compute the power-effective wind speed across the rotor
+        rotor_average_velocities = average_velocity(
+            velocities=velocities,
+            method=average_method,
+            cubature_weights=cubature_weights,
+        )
+
+        rotor_effective_velocities = rotor_velocity_air_density_correction(
+            velocities=rotor_average_velocities,
+            air_density=air_density,
+            ref_air_density=power_thrust_table["ref_air_density"]
+        )
+
+        rotor_effective_velocities = rotor_velocity_yaw_correction(
+            pP=power_thrust_table["pP"],
+            yaw_angles=yaw_angles,
+            rotor_effective_velocities=rotor_effective_velocities,
+        )
+
+        rotor_effective_velocities = rotor_velocity_tilt_correction(
+            tilt_angles=tilt_angles,
+            ref_tilt=power_thrust_table["ref_tilt"],
+            pT=power_thrust_table["pT"],
+            tilt_interp=tilt_interp,
+            correct_cp_ct_for_tilt=correct_cp_ct_for_tilt,
+            rotor_effective_velocities=rotor_effective_velocities,
+        )
+
+        # Compute power
+        power = power_interpolator(rotor_effective_velocities) * 1e3 # Convert to W
+
+        return power
+
+    def thrust_coefficient(
+        power_thrust_table: dict,
+        velocities: NDArrayFloat,
+        yaw_angles: NDArrayFloat,
+        tilt_angles: NDArrayFloat,
+        tilt_interp: NDArrayObject,
+        average_method: str = "cubic-mean",
+        cubature_weights: NDArrayFloat | None = None,
+        correct_cp_ct_for_tilt: bool = False,
+        **_ # <- Allows other models to accept other keyword arguments
+    ):
+        # Construct thrust coefficient interpolant
+        thrust_coefficient_interpolator = interp1d(
+            power_thrust_table["wind_speed"],
+            power_thrust_table["thrust_coefficient"],
+            fill_value=0.0001,
+            bounds_error=False,
+        )
+
+        # Compute the effective wind speed across the rotor
+        rotor_average_velocities = average_velocity(
+            velocities=velocities,
+            method=average_method,
+            cubature_weights=cubature_weights,
+        )
+
+        # TODO: Do we need an air density correction here?
+        thrust_coefficient = thrust_coefficient_interpolator(rotor_average_velocities)
+        thrust_coefficient = np.clip(thrust_coefficient, 0.0001, 0.9999)
+
+        # Apply tilt and yaw corrections
+        # Compute the tilt, if using floating turbines
+        old_tilt_angles = copy.deepcopy(tilt_angles)
+        tilt_angles = compute_tilt_angles_for_floating_turbines(
+            tilt_angles=tilt_angles,
+            tilt_interp=tilt_interp,
+            rotor_effective_velocities=rotor_average_velocities,
+        )
+        # Only update tilt angle if requested (if the tilt isn't accounted for in the Ct curve)
+        tilt_angles = np.where(correct_cp_ct_for_tilt, tilt_angles, old_tilt_angles)
+
+        thrust_coefficient = (
+            thrust_coefficient
+            * cosd(yaw_angles)
+            * cosd(tilt_angles - power_thrust_table["ref_tilt"])
+        )
+
+        return thrust_coefficient
+
+    def axial_induction(
+        power_thrust_table: dict,
+        velocities: NDArrayFloat,
+        yaw_angles: NDArrayFloat,
+        tilt_angles: NDArrayFloat,
+        tilt_interp: NDArrayObject,
+        average_method: str = "cubic-mean",
+        cubature_weights: NDArrayFloat | None = None,
+        correct_cp_ct_for_tilt: bool = False,
+        **_ # <- Allows other models to accept other keyword arguments
+    ):
+
+        thrust_coefficient = CosineLossTurbine.thrust_coefficient(
+            power_thrust_table=power_thrust_table,
+            velocities=velocities,
+            yaw_angles=yaw_angles,
+            tilt_angles=tilt_angles,
+            tilt_interp=tilt_interp,
+            average_method=average_method,
+            cubature_weights=cubature_weights,
+            correct_cp_ct_for_tilt=correct_cp_ct_for_tilt
+        )
+
+        misalignment_loss = cosd(yaw_angles) * cosd(tilt_angles - power_thrust_table["ref_tilt"])
+        return 0.5 / misalignment_loss * (1 - np.sqrt(1 - thrust_coefficient * misalignment_loss))
diff --git a/floris/simulation/turbine/turbine.py b/floris/simulation/turbine/turbine.py
new file mode 100644
index 000000000..d9aa76999
--- /dev/null
+++ b/floris/simulation/turbine/turbine.py
@@ -0,0 +1,624 @@
+# Copyright 2021 NREL
+
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+# See https://floris.readthedocs.io for documentation
+
+from __future__ import annotations
+
+import copy
+from collections.abc import Callable, Iterable
+from pathlib import Path
+
+import attrs
+import numpy as np
+import pandas as pd
+from attrs import define, field
+from scipy.interpolate import interp1d
+
+from floris.simulation import BaseClass
+from floris.simulation.turbine import (
+    CosineLossTurbine,
+    SimpleTurbine,
+)
+from floris.type_dec import (
+    convert_to_path,
+    floris_numeric_dict_converter,
+    NDArrayBool,
+    NDArrayFilter,
+    NDArrayFloat,
+    NDArrayInt,
+    NDArrayObject,
+)
+from floris.utilities import cosd
+
+
+TURBINE_MODEL_MAP = {
+    "power_thrust_model": {
+        "simple": SimpleTurbine,
+        "cosine-loss": CosineLossTurbine
+    },
+}
+
+
+def select_multidim_condition(
+    condition: dict | tuple,
+    specified_conditions: Iterable[tuple]
+) -> tuple:
+    """
+    Convert condition to the type expected by power_thrust_table and select
+    nearest specified condition
+    """
+    if type(condition) is tuple:
+        pass
+    elif type(condition) is dict:
+        condition = tuple(condition.values())
+    else:
+        raise TypeError("condition should be of type dict or tuple.")
+
+    # Find the nearest key to the specified conditions.
+    specified_conditions = np.array(specified_conditions)
+
+    # Find the nearest key to the specified conditions.
+    nearest_condition = np.zeros_like(condition)
+    for i, c in enumerate(condition):
+        nearest_condition[i] = (
+            specified_conditions[:, i][np.absolute(specified_conditions[:, i] - c).argmin()]
+        )
+
+    return tuple(nearest_condition)
+
+
+def power(
+    velocities: NDArrayFloat,
+    air_density: float,
+    power_functions: dict[str, Callable],
+    yaw_angles: NDArrayFloat,
+    tilt_angles: NDArrayFloat,
+    tilt_interps: dict[str, interp1d],
+    turbine_type_map: NDArrayObject,
+    turbine_power_thrust_tables: dict,
+    ix_filter: NDArrayInt | Iterable[int] | None = None,
+    average_method: str = "cubic-mean",
+    cubature_weights: NDArrayFloat | None = None,
+    correct_cp_ct_for_tilt: bool = False,
+    multidim_condition: tuple | None = None, # Assuming only one condition at a time?
+) -> NDArrayFloat:
+    """Power produced by a turbine adjusted for yaw and tilt. Value
+    given in Watts.
+
+    Args:
+        velocities (NDArrayFloat[n_findex, n_turbines, n_grid, n_grid]): The velocities at a
+            turbine.
+        air_density (float): air density for simulation [kg/m^3]
+        power_functions (dict[str, Callable]): A dictionary of power functions for
+            each turbine type. Keys are the turbine type and values are the callable functions.
+        yaw_angles (NDArrayFloat[findex, turbines]): The yaw angle for each turbine.
+        tilt_angles (NDArrayFloat[findex, turbines]): The tilt angle for each turbine.
+        tilt_interps (Iterable[tuple]): The tilt interpolation functions for each
+            turbine.
+        turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition for
+            each turbine.
+        turbine_power_thrust_tables: Reference data for the power and thrust representation
+        ix_filter (NDArrayInt, optional): The boolean array, or
+            integer indices to filter out before calculation. Defaults to None.
+        average_method (str, optional): The method for averaging over turbine rotor points
+            to determine a rotor-average wind speed. Defaults to "cubic-mean".
+        cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to
+            None.
+        multidim_condition (tuple | None): The condition tuple used to select the appropriate
+            thrust coefficient relationship for multidimensional power/thrust tables. Defaults to
+            None.
+
+    Returns:
+        NDArrayFloat: The power, in Watts, for each turbine after adjusting for yaw and tilt.
+    """
+    # TODO: Change the order of input arguments to be consistent with the other
+    # utility functions - velocities first...
+    # Update to power calculation which replaces the fixed pP exponent with
+    # an exponent pW, that changes the effective wind speed input to the power
+    # calculation, rather than scaling the power.  This better handles power
+    # loss to yaw in above rated conditions
+    #
+    # based on the paper "Optimising yaw control at wind farm level" by
+    # Ervin Bossanyi
+
+    # Down-select inputs if ix_filter is given
+    if ix_filter is not None:
+        velocities = velocities[:, ix_filter]
+        yaw_angles = yaw_angles[:, ix_filter]
+        tilt_angles = tilt_angles[:, ix_filter]
+        turbine_type_map = turbine_type_map[:, ix_filter]
+        if type(correct_cp_ct_for_tilt) is bool:
+            pass
+        else:
+            correct_cp_ct_for_tilt = correct_cp_ct_for_tilt[:, ix_filter]
+
+    # Loop over each turbine type given to get power for all turbines
+    p = np.zeros(np.shape(velocities)[0:2])
+    turb_types = np.unique(turbine_type_map)
+    for turb_type in turb_types:
+        # Handle possible multidimensional power thrust tables
+        if "power" in turbine_power_thrust_tables[turb_type]: # normal
+            power_thrust_table = turbine_power_thrust_tables[turb_type]
+        else: # assumed multidimensional, use multidim lookup
+            # Currently, only works for single mutlidim condition. May need to
+            # loop in the case where there are multiple conditions.
+            multidim_condition = select_multidim_condition(
+                multidim_condition,
+                list(turbine_power_thrust_tables[turb_type].keys())
+            )
+            power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition]
+
+        # Construct full set of possible keyword arguments for power()
+        power_model_kwargs = {
+            "power_thrust_table": power_thrust_table,
+            "velocities": velocities,
+            "air_density": air_density,
+            "yaw_angles": yaw_angles,
+            "tilt_angles": tilt_angles,
+            "tilt_interp": tilt_interps[turb_type],
+            "average_method": average_method,
+            "cubature_weights": cubature_weights,
+            "correct_cp_ct_for_tilt": correct_cp_ct_for_tilt,
+        }
+
+        # Using a masked array, apply the power for all turbines of the current
+        # type to the main power
+        p += power_functions[turb_type](**power_model_kwargs) * (turbine_type_map == turb_type)
+
+    return p
+
+
+def thrust_coefficient(
+    velocities: NDArrayFloat,
+    yaw_angles: NDArrayFloat,
+    tilt_angles: NDArrayFloat,
+    thrust_coefficient_functions: dict[str, Callable],
+    tilt_interps: dict[str, interp1d],
+    correct_cp_ct_for_tilt: NDArrayBool,
+    turbine_type_map: NDArrayObject,
+    turbine_power_thrust_tables: dict,
+    ix_filter: NDArrayFilter | Iterable[int] | None = None,
+    average_method: str = "cubic-mean",
+    cubature_weights: NDArrayFloat | None = None,
+    multidim_condition: tuple | None = None, # Assuming only one condition at a time?
+) -> NDArrayFloat:
+
+    """Thrust coefficient of a turbine.
+    The value is obtained from the coefficient of thrust specified by the callables specified
+    in the thrust_coefficient_functions.
+
+    Args:
+        velocities (NDArrayFloat[findex, turbines, grid1, grid2]): The velocity field at
+            a turbine.
+        yaw_angles (NDArrayFloat[findex, turbines]): The yaw angle for each turbine.
+        tilt_angles (NDArrayFloat[findex, turbines]): The tilt angle for each turbine.
+        thrust_coefficient_functions (dict): The thrust coefficient functions for each turbine. Keys
+            are the turbine type string and values are the callable functions.
+        tilt_interps (Iterable[tuple]): The tilt interpolation functions for each
+            turbine.
+        correct_cp_ct_for_tilt (NDArrayBool[findex, turbines]): Boolean for determining if the
+            turbines Cp and Ct should be corrected for tilt.
+        turbine_type_map: (NDArrayObject[findex, turbines]): The Turbine type definition
+            for each turbine.
+        ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or
+            integer indices as an iterable of array to filter out before calculation.
+            Defaults to None.
+        average_method (str, optional): The method for averaging over turbine rotor points
+            to determine a rotor-average wind speed. Defaults to "cubic-mean".
+        cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to
+            None.
+        multidim_condition (tuple | None): The condition tuple used to select the appropriate
+            thrust coefficient relationship for multidimensional power/thrust tables. Defaults to
+            None.
+
+    Returns:
+        NDArrayFloat: Coefficient of thrust for each requested turbine.
+    """
+
+    # Down-select inputs if ix_filter is given
+    if ix_filter is not None:
+        velocities = velocities[:, ix_filter]
+        yaw_angles = yaw_angles[:, ix_filter]
+        tilt_angles = tilt_angles[:, ix_filter]
+        turbine_type_map = turbine_type_map[:, ix_filter]
+        if type(correct_cp_ct_for_tilt) is bool:
+            pass
+        else:
+            correct_cp_ct_for_tilt = correct_cp_ct_for_tilt[:, ix_filter]
+
+    # Loop over each turbine type given to get thrust coefficient for all turbines
+    thrust_coefficient = np.zeros(np.shape(velocities)[0:2])
+    turb_types = np.unique(turbine_type_map)
+    for turb_type in turb_types:
+        # Handle possible multidimensional power thrust tables
+        if "thrust_coefficient" in turbine_power_thrust_tables[turb_type]: # normal
+            power_thrust_table = turbine_power_thrust_tables[turb_type]
+        else: # assumed multidimensional, use multidim lookup
+            # Currently, only works for single mutlidim condition. May need to
+            # loop in the case where there are multiple conditions.
+            multidim_condition = select_multidim_condition(
+                multidim_condition,
+                list(turbine_power_thrust_tables[turb_type].keys())
+            )
+            power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition]
+
+        # Construct full set of possible keyword arguments for thrust_coefficient()
+        thrust_model_kwargs = {
+            "power_thrust_table": power_thrust_table,
+            "velocities": velocities,
+            "yaw_angles": yaw_angles,
+            "tilt_angles": tilt_angles,
+            "tilt_interp": tilt_interps[turb_type],
+            "average_method": average_method,
+            "cubature_weights": cubature_weights,
+            "correct_cp_ct_for_tilt": correct_cp_ct_for_tilt,
+        }
+
+        # Using a masked array, apply the thrust coefficient for all turbines of the current
+        # type to the main thrust coefficient array
+        thrust_coefficient += (
+            thrust_coefficient_functions[turb_type](**thrust_model_kwargs)
+            * (turbine_type_map == turb_type)
+        )
+
+    return thrust_coefficient
+
+
+def axial_induction(
+    velocities: NDArrayFloat,
+    yaw_angles: NDArrayFloat,
+    tilt_angles: NDArrayFloat,
+    axial_induction_functions: dict,
+    tilt_interps: NDArrayObject,
+    correct_cp_ct_for_tilt: NDArrayBool,
+    turbine_type_map: NDArrayObject,
+    turbine_power_thrust_tables: dict,
+    ix_filter: NDArrayFilter | Iterable[int] | None = None,
+    average_method: str = "cubic-mean",
+    cubature_weights: NDArrayFloat | None = None,
+    multidim_condition: tuple | None = None, # Assuming only one condition at a time?
+) -> NDArrayFloat:
+    """Axial induction factor of the turbine incorporating
+    the thrust coefficient and yaw angle.
+
+    Args:
+        velocities (NDArrayFloat): The velocity field at each turbine; should be shape:
+            (number of turbines, ngrid, ngrid), or (ngrid, ngrid) for a single turbine.
+        yaw_angles (NDArrayFloat[findex, turbines]): The yaw angle for each turbine.
+        tilt_angles (NDArrayFloat[findex, turbines]): The tilt angle for each turbine.
+        axial_induction_functions (dict): The axial induction functions for each turbine. Keys are
+            the turbine type string and values are the callable functions.
+        tilt_interps (Iterable[tuple]): The tilt interpolation functions for each
+            turbine.
+        correct_cp_ct_for_tilt (NDArrayBool[findex, turbines]): Boolean for determining if the
+            turbines Cp and Ct should be corrected for tilt.
+        turbine_type_map: (NDArrayObject[findex, turbines]): The Turbine type definition
+            for each turbine.
+        ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or
+            integer indices (as an array or iterable) to filter out before calculation.
+            Defaults to None.
+        average_method (str, optional): The method for averaging over turbine rotor points
+            to determine a rotor-average wind speed. Defaults to "cubic-mean".
+        cubature_weights (NDArrayFloat | None): Weights for cubature averaging methods. Defaults to
+            None.
+        multidim_condition (tuple | None): The condition tuple used to select the appropriate
+            thrust coefficient relationship for multidimensional power/thrust tables. Defaults to
+            None.
+
+    Returns:
+        Union[float, NDArrayFloat]: [description]
+    """
+
+    # Down-select inputs if ix_filter is given
+    if ix_filter is not None:
+        velocities = velocities[:, ix_filter]
+        yaw_angles = yaw_angles[:, ix_filter]
+        tilt_angles = tilt_angles[:, ix_filter]
+        turbine_type_map = turbine_type_map[:, ix_filter]
+        if type(correct_cp_ct_for_tilt) is bool:
+            pass
+        else:
+            correct_cp_ct_for_tilt = correct_cp_ct_for_tilt[:, ix_filter]
+
+    # Loop over each turbine type given to get axial induction for all turbines
+    axial_induction = np.zeros(np.shape(velocities)[0:2])
+    turb_types = np.unique(turbine_type_map)
+    for turb_type in turb_types:
+        # Handle possible multidimensional power thrust tables
+        if "thrust_coefficient" in turbine_power_thrust_tables[turb_type]: # normal
+            power_thrust_table = turbine_power_thrust_tables[turb_type]
+        else: # assumed multidimensional, use multidim lookup
+            # Currently, only works for single mutlidim condition. May need to
+            # loop in the case where there are multiple conditions.
+            multidim_condition = select_multidim_condition(
+                multidim_condition,
+                list(turbine_power_thrust_tables[turb_type].keys())
+            )
+            power_thrust_table = turbine_power_thrust_tables[turb_type][multidim_condition]
+
+        # Construct full set of possible keyword arguments for thrust_coefficient()
+        axial_induction_model_kwargs = {
+            "power_thrust_table": power_thrust_table,
+            "velocities": velocities,
+            "yaw_angles": yaw_angles,
+            "tilt_angles": tilt_angles,
+            "tilt_interp": tilt_interps[turb_type],
+            "average_method": average_method,
+            "cubature_weights": cubature_weights,
+            "correct_cp_ct_for_tilt": correct_cp_ct_for_tilt,
+        }
+
+        # Using a masked array, apply the thrust coefficient for all turbines of the current
+        # type to the main thrust coefficient array
+        axial_induction += (
+            axial_induction_functions[turb_type](**axial_induction_model_kwargs)
+            * (turbine_type_map == turb_type)
+        )
+
+    return axial_induction
+
+
+@define
+class Turbine(BaseClass):
+    """
+    A class containing the parameters and infrastructure to model a wind turbine's performance
+    for a particular atmospheric condition.
+
+    Args:
+        turbine_type (str): An identifier for this type of turbine such as "NREL_5MW".
+        rotor_diameter (float): The rotor diameter in meters.
+        hub_height (float): The hub height in meters.
+        TSR (float): The Tip Speed Ratio of the turbine.
+        generator_efficiency (float): The efficiency of the generator used to scale
+            power production.
+        power_thrust_table (dict[str, float]): Contains power coefficient and thrust coefficient
+            values at a series of wind speeds to define the turbine performance.
+            The dictionary must have the following three keys with equal length values:
+                {
+                    "wind_speeds": List[float],
+                    "power": List[float],
+                    "thrust": List[float],
+                }
+            or, contain a key "power_thrust_data_file" pointing to the power/thrust data.
+            Optionally, power_thrust_table may include parameters for use in the turbine submodel,
+            for example:
+                pP (float): The cosine exponent relating the yaw misalignment angle to turbine
+                    power.
+                pT (float): The cosine exponent relating the rotor tilt angle to turbine
+                    power.
+                ref_air_density (float): The density at which the provided Cp and Ct curves are
+                    defined.
+                ref_tilt (float): The implicit tilt of the turbine for which the Cp and Ct
+                    curves are defined. This is typically the nacelle tilt.
+        correct_cp_ct_for_tilt (bool): A flag to indicate whether to correct Cp and Ct for tilt
+            usually for a floating turbine.
+            Optional, defaults to False.
+        floating_tilt_table (dict[str, float]): Look up table of tilt angles at a series of
+            wind speeds. The dictionary must have the following keys with equal length values:
+                {
+                    "wind_speeds": List[float],
+                    "tilt": List[float],
+                }
+            Required if `correct_cp_ct_for_tilt = True`. Defaults to None.
+        multi_dimensional_cp_ct (bool): Use a multidimensional power_thrust_table. Defaults to
+            False.
+    """
+    turbine_type: str = field()
+    rotor_diameter: float = field()
+    hub_height: float = field()
+    TSR: float = field()
+    generator_efficiency: float = field()
+    power_thrust_table: dict = field(default={}) # conversion to numpy in __post_init__
+    power_thrust_model: str = field(default="cosine-loss")
+
+    correct_cp_ct_for_tilt: bool = field(default=False)
+    floating_tilt_table: dict[str, NDArrayFloat] | None = field(default=None)
+
+    # Even though this Turbine class does not support the multidimensional features as they
+    # are implemented in TurbineMultiDim, providing the following two attributes here allows
+    # the turbine data inputs to keep the multidimensional Cp and Ct curve but switch them off
+    # with multi_dimensional_cp_ct = False
+    multi_dimensional_cp_ct: bool = field(default=False)
+
+    # Initialized in the post_init function
+    rotor_radius: float = field(init=False)
+    rotor_area: float = field(init=False)
+    thrust_coefficient_function: Callable = field(init=False)
+    axial_induction_function: Callable = field(init=False)
+    power_function: Callable = field(init=False)
+    tilt_interp: interp1d = field(init=False, default=None)
+    power_thrust_data_file: str = field(default=None)
+
+    # Only used by mutlidimensional turbines
+    turbine_library_path: Path = field(
+        default=Path(__file__).parents[2] / "turbine_library",
+        converter=convert_to_path,
+        validator=attrs.validators.instance_of(Path)
+    )
+
+    # Not to be provided by the user
+    condition_keys: list[str] = field(init=False, factory=list)
+
+    def __attrs_post_init__(self) -> None:
+        self._initialize_power_thrust_functions()
+        self.__post_init__()
+
+    def __post_init__(self) -> None:
+        self._initialize_tilt_interpolation()
+        if self.multi_dimensional_cp_ct:
+            self._initialize_multidim_power_thrust_table()
+        else:
+            self.power_thrust_table = floris_numeric_dict_converter(self.power_thrust_table)
+
+    def _initialize_power_thrust_functions(self) -> None:
+        turbine_function_model = TURBINE_MODEL_MAP["power_thrust_model"][self.power_thrust_model]
+        self.thrust_coefficient_function = turbine_function_model.thrust_coefficient
+        self.axial_induction_function = turbine_function_model.axial_induction
+        self.power_function = turbine_function_model.power
+
+
+    def _initialize_tilt_interpolation(self) -> None:
+        # TODO:
+        # Remove any duplicate wind speed entries
+        # _, duplicate_filter = np.unique(self.wind_speeds, return_index=True)
+        # self.tilt = self.tilt[duplicate_filter]
+        # self.wind_speeds = self.wind_speeds[duplicate_filter]
+
+        if self.floating_tilt_table is not None:
+            self.floating_tilt_table = floris_numeric_dict_converter(self.floating_tilt_table)
+
+        # If defined, create a tilt interpolation function for floating turbines.
+        # fill_value currently set to apply the min or max tilt angles if outside
+        # of the interpolation range.
+        if self.correct_cp_ct_for_tilt:
+            self.tilt_interp = interp1d(
+                self.floating_tilt_table["wind_speed"],
+                self.floating_tilt_table["tilt"],
+                fill_value=(0.0, self.floating_tilt_table["tilt"][-1]),
+                bounds_error=False,
+            )
+
+    def _initialize_multidim_power_thrust_table(self):
+        # Collect reference information
+        power_thrust_table_ref = copy.deepcopy(self.power_thrust_table)
+        self.power_thrust_data_file = power_thrust_table_ref.pop("power_thrust_data_file")
+
+        # Solidify the data file path and name
+        self.power_thrust_data_file = self.turbine_library_path / self.power_thrust_data_file
+
+        # Read in the multi-dimensional data supplied by the user.
+        df = pd.read_csv(self.power_thrust_data_file)
+
+        # Down-select the DataFrame to have just the ws, Cp, and Ct values
+        index_col = df.columns.values[:-3]
+        self.condition_keys = index_col.tolist()
+        df2 = df.set_index(index_col.tolist())
+
+        # Loop over the multi-dimensional keys to get the correct ws/Cp/Ct data to make
+        # the thrust_coefficient and power interpolants.
+        power_thrust_table_ = {} # Reset
+        for key in df2.index.unique():
+            # Select the correct ws/Cp/Ct data
+            data = df2.loc[key]
+
+            # Build the interpolants
+            power_thrust_table_.update({
+                key: {
+                    "wind_speed": data['ws'].values,
+                    "power": (
+                        0.5 * self.rotor_area * data['Cp'].values * self.generator_efficiency
+                        * data['ws'].values ** 3 * power_thrust_table_ref["ref_air_density"] / 1000
+                    ), # TODO: convert this to 'power' or 'P' in data tables, as per PR #765
+                    "thrust_coefficient": data['Ct'].values,
+                    **power_thrust_table_ref
+                },
+            })
+            # Add reference information at the lower level
+
+        # Set on-object version
+        self.power_thrust_table = power_thrust_table_
+
+    @power_thrust_table.validator
+    def check_power_thrust_table(self, instance: attrs.Attribute, value: dict) -> None:
+        """
+        Verify that the power and thrust tables are given with arrays of equal length
+        to the wind speed array.
+        """
+
+        if self.multi_dimensional_cp_ct:
+            if isinstance(list(value.keys())[0], tuple):
+                value = list(value.values())[0] # Check the first entry of multidim
+            elif "power_thrust_data_file" in value.keys():
+                return None
+            else:
+                raise ValueError(
+                    "power_thrust_data_file must be defined if multi_dimensional_cp_ct is True."
+                )
+
+        if not {"wind_speed", "power", "thrust_coefficient"} <= set(value.keys()):
+            raise ValueError(
+                """
+                power_thrust_table dictionary must contain:
+                    {
+                        "wind_speed": List[float],
+                        "power": List[float],
+                        "thrust_coefficient": List[float],
+                    }
+                """
+            )
+
+    @rotor_diameter.validator
+    def reset_rotor_diameter_dependencies(self, instance: attrs.Attribute, value: float) -> None:
+        """Resets the `rotor_radius` and `rotor_area` attributes."""
+        # Temporarily turn off validators to avoid infinite recursion
+        with attrs.validators.disabled():
+            # Reset the values
+            self.rotor_radius = value / 2.0
+            self.rotor_area = np.pi * self.rotor_radius ** 2.0
+
+    @rotor_radius.validator
+    def reset_rotor_radius(self, instance: attrs.Attribute, value: float) -> None:
+        """
+        Resets the `rotor_diameter` value to trigger the recalculation of
+        `rotor_diameter`, `rotor_radius` and `rotor_area`.
+        """
+        self.rotor_diameter = value * 2.0
+
+    @rotor_area.validator
+    def reset_rotor_area(self, instance: attrs.Attribute, value: float) -> None:
+        """
+        Resets the `rotor_radius` value to trigger the recalculation of
+        `rotor_diameter`, `rotor_radius` and `rotor_area`.
+        """
+        self.rotor_radius = (value / np.pi) ** 0.5
+
+    @floating_tilt_table.validator
+    def check_floating_tilt_table(self, instance: attrs.Attribute, value: dict | None) -> None:
+        """
+        If the tilt / wind_speed table is defined, verify that the tilt and
+        wind_speed arrays are the same length.
+        """
+        if value is None:
+            return
+
+        if len(value.keys()) != 2 or set(value.keys()) != {"wind_speed", "tilt"}:
+            raise ValueError(
+                """
+                floating_tilt_table dictionary must have the form:
+                    {
+                        "wind_speed": List[float],
+                        "tilt": List[float],
+                    }
+                """
+            )
+
+        if any(len(np.shape(e)) > 1 for e in (value["tilt"], value["wind_speed"])):
+            raise ValueError("tilt and wind_speed inputs must be 1-D.")
+
+        if len( {len(value["tilt"]), len(value["wind_speed"])} ) > 1:
+            raise ValueError("tilt and wind_speed inputs must be the same size.")
+
+    @correct_cp_ct_for_tilt.validator
+    def check_for_cp_ct_correct_flag_if_floating(
+        self,
+        instance: attrs.Attribute,
+        value: bool
+    ) -> None:
+        """
+        Check that the boolean flag exists for correcting Cp/Ct for tilt
+        if a tile/wind_speed table is also defined.
+        """
+        if self.correct_cp_ct_for_tilt and self.floating_tilt_table is None:
+            raise ValueError(
+                "To enable the Cp and Ct tilt correction, a tilt table must be given."
+            )
diff --git a/floris/simulation/turbine_multi_dim.py b/floris/simulation/turbine_multi_dim.py
deleted file mode 100644
index 3248ff4e4..000000000
--- a/floris/simulation/turbine_multi_dim.py
+++ /dev/null
@@ -1,498 +0,0 @@
-# Copyright 2023 NREL
-
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not
-# use this file except in compliance with the License. You may obtain a copy of
-# the License at http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
-# License for the specific language governing permissions and limitations under
-# the License.
-
-# See https://floris.readthedocs.io for documentation
-
-from __future__ import annotations
-
-import copy
-from collections.abc import Iterable
-from pathlib import Path
-
-import attrs
-import numpy as np
-import pandas as pd
-from attrs import define, field
-from flatten_dict import flatten
-from scipy.interpolate import interp1d
-
-from floris.simulation import (
-    average_velocity,
-    compute_tilt_angles_for_floating_turbines,
-    Turbine,
-)
-from floris.type_dec import (
-    convert_to_path,
-    NDArrayBool,
-    NDArrayFilter,
-    NDArrayFloat,
-    NDArrayInt,
-    NDArrayObject,
-)
-from floris.utilities import cosd
-
-
-def power_multidim(
-    ref_air_density: float,
-    rotor_effective_velocities: NDArrayFloat,
-    power_interp: NDArrayObject,
-    ix_filter: NDArrayInt | Iterable[int] | None = None,
-) -> NDArrayFloat:
-    """Power produced by a turbine defined with multi-dimensional
-    Cp/Ct values, adjusted for yaw and tilt. Value given in Watts.
-
-    Args:
-        ref_air_densities (NDArrayFloat[wd, ws, turbines]): The reference density for each turbine
-        rotor_effective_velocities (NDArrayFloat[wd, ws, turbines, grid1, grid2]): The rotor
-            effective velocities at a turbine.
-        power_interp (NDArrayObject[wd, ws, turbines]): The power interpolation function
-            for each turbine.
-        ix_filter (NDArrayInt, optional): The boolean array, or
-            integer indices to filter out before calculation. Defaults to None.
-
-    Returns:
-        NDArrayFloat: The power, in Watts, for each turbine after adjusting for yaw and tilt.
-    """
-    # TODO: Change the order of input arguments to be consistent with the other
-    # utility functions - velocities first...
-    # Update to power calculation which replaces the fixed pP exponent with
-    # an exponent pW, that changes the effective wind speed input to the power
-    # calculation, rather than scaling the power.  This better handles power
-    # loss to yaw in above rated conditions
-    #
-    # based on the paper "Optimising yaw control at wind farm level" by
-    # Ervin Bossanyi
-
-    # TODO: check this - where is it?
-    # P = 1/2 rho A V^3 Cp
-
-    # Down-select inputs if ix_filter is given
-    if ix_filter is not None:
-        power_interp = power_interp[:, ix_filter]
-        rotor_effective_velocities = rotor_effective_velocities[:, ix_filter]
-    # Loop over each turbine to get power for all turbines
-    p = np.zeros(np.shape(rotor_effective_velocities))
-    for i, findex in enumerate(power_interp):
-        for j, turb in enumerate(findex):
-            p[i, j] = power_interp[i, j](rotor_effective_velocities[i, j])
-
-    return p * ref_air_density
-
-
-def Ct_multidim(
-    velocities: NDArrayFloat,
-    yaw_angle: NDArrayFloat,
-    tilt_angle: NDArrayFloat,
-    ref_tilt: NDArrayFloat,
-    fCt: list,
-    tilt_interp: NDArrayObject,
-    correct_cp_ct_for_tilt: NDArrayBool,
-    turbine_type_map: NDArrayObject,
-    ix_filter: NDArrayFilter | Iterable[int] | None = None,
-    average_method: str = "cubic-mean",
-    cubature_weights: NDArrayFloat | None = None
-) -> NDArrayFloat:
-
-    """Thrust coefficient of a turbine defined with multi-dimensional
-    Cp/Ct values, incorporating the yaw angle. The value is interpolated
-    from the coefficient of thrust vs wind speed table using the rotor
-    swept area average velocity.
-
-    Args:
-        velocities (NDArrayFloat[wd, ws, turbines, grid1, grid2]): The velocity field at
-            a turbine.
-        yaw_angle (NDArrayFloat[wd, ws, turbines]): The yaw angle for each turbine.
-        tilt_angle (NDArrayFloat[wd, ws, turbines]): The tilt angle for each turbine.
-        ref_tilt (NDArrayFloat[wd, ws, turbines]): The reference tilt angle for each turbine
-            that the Cp/Ct tables are defined at.
-        fCt (list): The thrust coefficient interpolation functions for each turbine.
-        tilt_interp (Iterable[tuple]): The tilt interpolation functions for each
-            turbine.
-        correct_cp_ct_for_tilt (NDArrayBool[wd, ws, turbines]): Boolean for determining if the
-            turbines Cp and Ct should be corrected for tilt.
-        turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition
-            for each turbine.
-        ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or
-            integer indices as an iterable of array to filter out before calculation.
-            Defaults to None.
-
-    Returns:
-        NDArrayFloat: Coefficient of thrust for each requested turbine.
-    """
-
-    if isinstance(yaw_angle, list):
-        yaw_angle = np.array(yaw_angle)
-
-    if isinstance(tilt_angle, list):
-        tilt_angle = np.array(tilt_angle)
-
-    # Down-select inputs if ix_filter is given
-    if ix_filter is not None:
-        velocities = velocities[:, ix_filter]
-        yaw_angle = yaw_angle[:, ix_filter]
-        tilt_angle = tilt_angle[:, ix_filter]
-        ref_tilt = ref_tilt[:, ix_filter]
-        fCt = fCt[:, ix_filter]
-        turbine_type_map = turbine_type_map[:, ix_filter]
-        correct_cp_ct_for_tilt = correct_cp_ct_for_tilt[:, ix_filter]
-
-    average_velocities = average_velocity(
-        velocities,
-        method=average_method,
-        cubature_weights=cubature_weights
-    )
-
-    # Compute the tilt, if using floating turbines
-    old_tilt_angle = copy.deepcopy(tilt_angle)
-    tilt_angle = compute_tilt_angles_for_floating_turbines(
-        turbine_type_map,
-        tilt_angle,
-        tilt_interp,
-        average_velocities,
-    )
-    # Only update tilt angle if requested (if the tilt isn't accounted for in the Ct curve)
-    tilt_angle = np.where(correct_cp_ct_for_tilt, tilt_angle, old_tilt_angle)
-
-    # Loop over each turbine to get thrust coefficient for all turbines
-    thrust_coefficient = np.zeros(np.shape(average_velocities))
-    for i, findex in enumerate(fCt):
-        for j, turb in enumerate(findex):
-            thrust_coefficient[i, j] = fCt[i, j](average_velocities[i, j])
-    thrust_coefficient = np.clip(thrust_coefficient, 0.0001, 0.9999)
-    effective_thrust = thrust_coefficient * cosd(yaw_angle) * cosd(tilt_angle - ref_tilt)
-    return effective_thrust
-
-
-def axial_induction_multidim(
-    velocities: NDArrayFloat,  # (wind directions, wind speeds, turbines, grid, grid)
-    yaw_angle: NDArrayFloat,  # (wind directions, wind speeds, turbines)
-    tilt_angle: NDArrayFloat,  # (wind directions, wind speeds, turbines)
-    ref_tilt: NDArrayFloat,
-    fCt: list,  # (turbines)
-    tilt_interp: NDArrayObject,  # (turbines)
-    correct_cp_ct_for_tilt: NDArrayBool, # (wind directions, wind speeds, turbines)
-    turbine_type_map: NDArrayObject, # (wind directions, 1, turbines)
-    ix_filter: NDArrayFilter | Iterable[int] | None = None,
-    average_method: str = "cubic-mean",
-    cubature_weights: NDArrayFloat | None = None
-) -> NDArrayFloat:
-    """Axial induction factor of the turbines defined with multi-dimensional
-    Cp/Ct values, incorporating the thrust coefficient and yaw angle.
-
-    Args:
-        velocities (NDArrayFloat): The velocity field at each turbine; should be shape:
-            (number of turbines, ngrid, ngrid), or (ngrid, ngrid) for a single turbine.
-        yaw_angle (NDArrayFloat[wd, ws, turbines]): The yaw angle for each turbine.
-        tilt_angle (NDArrayFloat[wd, ws, turbines]): The tilt angle for each turbine.
-        ref_tilt (NDArrayFloat[wd, ws, turbines]): The reference tilt angle for each turbine
-            that the Cp/Ct tables are defined at.
-        fCt (list): The thrust coefficient interpolation functions for each turbine.
-        tilt_interp (Iterable[tuple]): The tilt interpolation functions for each
-            turbine.
-        correct_cp_ct_for_tilt (NDArrayBool[wd, ws, turbines]): Boolean for determining if the
-            turbines Cp and Ct should be corrected for tilt.
-        turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition
-            for each turbine.
-        ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or
-            integer indices (as an array or iterable) to filter out before calculation.
-            Defaults to None.
-
-    Returns:
-        Union[float, NDArrayFloat]: [description]
-    """
-
-    if isinstance(yaw_angle, list):
-        yaw_angle = np.array(yaw_angle)
-
-    # TODO: Should the tilt_angle used for the return calculation be modified the same as the
-    # tilt_angle in Ct, if the user has supplied a tilt/wind_speed table?
-    if isinstance(tilt_angle, list):
-        tilt_angle = np.array(tilt_angle)
-
-    # Get Ct first before modifying any data
-    thrust_coefficient = Ct_multidim(
-        velocities,
-        yaw_angle,
-        tilt_angle,
-        ref_tilt,
-        fCt,
-        tilt_interp,
-        correct_cp_ct_for_tilt,
-        turbine_type_map,
-        ix_filter,
-        average_method,
-        cubature_weights
-    )
-
-    # Then, process the input arguments as needed for this function
-    if ix_filter is not None:
-        yaw_angle = yaw_angle[:, ix_filter]
-        tilt_angle = tilt_angle[:, ix_filter]
-        ref_tilt = ref_tilt[:, ix_filter]
-
-    return (
-        0.5
-        / (cosd(yaw_angle)
-        * cosd(tilt_angle - ref_tilt))
-        * (
-            1 - np.sqrt(
-                1 - thrust_coefficient * cosd(yaw_angle) * cosd(tilt_angle - ref_tilt)
-            )
-        )
-    )
-
-
-def multidim_Ct_down_select(
-    turbine_fCts,
-    conditions,
-) -> list:
-    """
-    Ct interpolants are down selected from the multi-dimensional Ct data
-    provided for the turbine based on the specified conditions.
-
-    Args:
-        turbine_fCts (NDArray[wd, ws, turbines]): The Ct interpolants generated from the
-            multi-dimensional Ct turbine data for all specified conditions.
-        conditions (dict): The conditions at which to determine which Ct interpolant to use.
-
-    Returns:
-        NDArray: The down selected Ct interpolants for the selected conditions.
-    """
-    downselect_turbine_fCts = np.empty_like(turbine_fCts)
-    # Loop over the wind directions, wind speeds, and turbines, finding the Ct interpolant
-    # that is closest to the specified multi-dimensional condition.
-    for i, findex in enumerate(turbine_fCts):
-        for j, turb in enumerate(findex):
-            # Get the interpolant keys in float type for comparison
-            keys_float = np.array([[float(v) for v in val] for val in turb.keys()])
-
-            # Find the nearest key to the specified conditions.
-            key_vals = []
-            for ii, cond in enumerate(conditions.values()):
-                key_vals.append(
-                    keys_float[:, ii][np.absolute(keys_float[:, ii] - cond).argmin()]
-                )
-
-            downselect_turbine_fCts[i, j] = turb[tuple(key_vals)]
-
-    return downselect_turbine_fCts
-
-
-def multidim_power_down_select(
-    power_interps,
-    conditions,
-) -> list:
-    """
-    Cp interpolants are down selected from the multi-dimensional Cp data
-    provided for the turbine based on the specified conditions.
-
-    Args:
-        power_interps (NDArray[wd, ws, turbines]): The power interpolants generated from the
-            multi-dimensional Cp turbine data for all specified conditions.
-        conditions (dict): The conditions at which to determine which Ct interpolant to use.
-
-    Returns:
-        NDArray: The down selected power interpolants for the selected conditions.
-    """
-    downselect_power_interps = np.empty_like(power_interps)
-    # Loop over the wind directions, wind speeds, and turbines, finding the power interpolant
-    # that is closest to the specified multi-dimensional condition.
-    for i, findex in enumerate(power_interps):
-        for j, turb in enumerate(findex):
-            # Get the interpolant keys in float type for comparison
-            keys_float = np.array([[float(v) for v in val] for val in turb.keys()])
-
-            # Find the nearest key to the specified conditions.
-            key_vals = []
-            for ii, cond in enumerate(conditions.values()):
-                key_vals.append(
-                    keys_float[:, ii][np.absolute(keys_float[:, ii] - cond).argmin()]
-                )
-
-            # Use the constructed key to choose the correct interpolant
-            downselect_power_interps[i, j] = turb[tuple(key_vals)]
-
-    return downselect_power_interps
-
-
-@define
-class MultiDimensionalPowerThrustTable():
-    """Helper class to convert the multi-dimensional inputs to a dictionary of objects.
-    """
-
-    @classmethod
-    def from_dataframe(self, df) -> None:
-        # Validate the dataframe
-        if not all(ele in df.columns.values.tolist() for ele in ["ws", "Cp", "Ct"]):
-            print(df.columns.values.tolist())
-            raise ValueError("Multidimensional data missing required ws/Cp/Ct data.")
-        if df.columns.values[-3:].tolist() != ["ws", "Cp", "Ct"]:
-            print(df.columns.values[-3:].tolist())
-            raise ValueError(
-                "Multidimensional data not in correct form. ws, Cp, and Ct must be "
-                "defined as the last 3 columns, in that order."
-            )
-
-        # Extract the supplied dimensions, minus the required ws, Cp, and Ct columns.
-        keys = df.columns.values[:-3].tolist()
-        values = [df[df.columns.values[i]].unique().tolist() for i in range(len(keys))]
-        values = [[str(val) for val in value] for value in values]
-
-        # Functions for recursively building a nested dictionary from
-        # an arbitrary number of paired-inputs.
-        def add_level(obj, k, v):
-            tmp = {}
-            for val in v:
-                tmp.update({val: []})
-            obj.update({k: tmp})
-            return obj
-
-        def add_sub_level(obj, k):
-            tmp = {}
-            for key in k:
-                tmp.update({key: obj})
-            return tmp
-
-        obj = {}
-        # Reverse the lists to start from the lowest level of the dictionary
-        keys.reverse()
-        values.reverse()
-        # Recursively build a nested dictionary from the user-supplied dimensions
-        for i, key in enumerate(keys):
-            if i == 0:
-                obj = add_level(obj, key, values[i])
-            else:
-                obj = add_sub_level(obj, values[i])
-                obj = {key: obj}
-
-        return flatten(obj)
-
-
-@define
-class TurbineMultiDimensional(Turbine):
-    """
-    Turbine is a class containing objects pertaining to the individual
-    turbines.
-
-    Turbine is a model class representing a particular wind turbine. It
-    is largely a container of data and parameters, but also contains
-    methods to probe properties for output.
-
-    Parameters:
-        rotor_diameter (:py:obj: float): The rotor diameter (m).
-        hub_height (:py:obj: float): The hub height (m).
-        pP (:py:obj: float): The cosine exponent relating the yaw
-            misalignment angle to power.
-        pT (:py:obj: float): The cosine exponent relating the rotor
-            tilt angle to power.
-        generator_efficiency (:py:obj: float): The generator
-            efficiency factor used to scale the power production.
-        ref_air_density (:py:obj: float): The density at which the provided
-            cp and ct is defined
-        power_thrust_table (PowerThrustTable): A dictionary containing the
-            following key-value pairs:
-
-            power (:py:obj: List[float]): The coefficient of power at
-                different wind speeds.
-            thrust (:py:obj: List[float]): The coefficient of thrust
-                at different wind speeds.
-            wind_speed (:py:obj: List[float]): The wind speeds for
-                which the power and thrust values are provided (m/s).
-        ngrid (*int*, optional): The square root of the number
-            of points to use on the turbine grid. This number will be
-            squared so that the points can be evenly distributed.
-            Defaults to 5.
-        rloc (:py:obj: float, optional): A value, from 0 to 1, that determines
-            the width/height of the grid of points on the rotor as a ratio of
-            the rotor radius.
-            Defaults to 0.5.
-        power_thrust_data_file (:py:obj:`str`): The path and name of the file containing the
-            multidimensional power thrust curve. The path may be an absolute location or a relative
-            path to where FLORIS is being run.
-        multi_dimensional_cp_ct (:py:obj:`bool`, optional): Indicates if the turbine definition is
-            single dimensional (False) or multidimensional (True).
-        turbine_library_path (:py:obj:`pathlib.Path`, optional): The
-            :py:attr:`Farm.turbine_library_path` or :py:attr:`Farm.internal_turbine_library_path`,
-            whichever is being used to load turbine definitions.
-            Defaults to the internal turbine library.
-    """
-    multi_dimensional_cp_ct: bool = field(default=False)
-    power_thrust_table: dict = field(default={})
-    # TODO power_thrust_data_file is actually required and should not default to None.
-    # However, the super class has optional attributes so a required attribute here breaks
-    power_thrust_data_file: str = field(default=None)
-    power_thrust_data: MultiDimensionalPowerThrustTable = field(default=None)
-    turbine_library_path: Path = field(
-        default=Path(__file__).parents[1] / "turbine_library",
-        converter=convert_to_path,
-        validator=attrs.validators.instance_of(Path)
-    )
-
-    # Not to be provided by the user
-    condition_keys: list[str] = field(init=False, factory=list)
-
-    def __attrs_post_init__(self) -> None:
-        super().__post_init__()
-
-        # Solidify the data file path and name
-        self.power_thrust_data_file = self.turbine_library_path / self.power_thrust_data_file
-
-        # Read in the multi-dimensional data supplied by the user.
-        df = pd.read_csv(self.power_thrust_data_file)
-
-        # Build the multi-dimensional power/thrust table
-        self.power_thrust_data = MultiDimensionalPowerThrustTable.from_dataframe(df)
-
-        # Create placeholders for the interpolation functions
-        self.fCt_interp = {}
-        self.power_interp = {}
-
-        # Down-select the DataFrame to have just the ws, Cp, and Ct values
-        index_col = df.columns.values[:-3]
-        self.condition_keys = index_col.tolist()
-        df2 = df.set_index(index_col.tolist())
-
-        # Loop over the multi-dimensional keys to get the correct ws/Cp/Ct data to make
-        # the Ct and power interpolants.
-        for key in df2.index.unique():
-            # Select the correct ws/Cp/Ct data
-            data = df2.loc[key]
-
-            # Build the interpolants
-            wind_speeds = data['ws'].values
-            cp_interp = interp1d(
-                wind_speeds,
-                data['Cp'].values,
-                fill_value=(0.0, 1.0),
-                bounds_error=False,
-            )
-            self.power_interp.update({
-                key: interp1d(
-                    wind_speeds,
-                    (
-                        0.5 * self.rotor_area
-                        * cp_interp(wind_speeds)
-                        * self.generator_efficiency
-                        * wind_speeds ** 3
-                    ),
-                    bounds_error=False,
-                    fill_value=0
-                )
-            })
-            self.fCt_interp.update({
-                key: interp1d(
-                    wind_speeds,
-                    data['Ct'].values,
-                    fill_value=(0.0001, 0.9999),
-                    bounds_error=False,
-                )
-            })
diff --git a/floris/tools/__init__.py b/floris/tools/__init__.py
index 4242e7be1..6a2cca91b 100644
--- a/floris/tools/__init__.py
+++ b/floris/tools/__init__.py
@@ -39,7 +39,6 @@
 from .floris_interface import FlorisInterface
 from .floris_interface_legacy_reader import FlorisInterfaceLegacyV2
 from .parallel_computing_interface import ParallelComputingInterface
-from .turbine_utilities import build_turbine_dict, check_smooth_power_curve
 from .uncertainty_interface import UncertaintyInterface
 from .visualization import (
     plot_rotor_values,
diff --git a/floris/tools/convert_turbine_v3_to_v4.py b/floris/tools/convert_turbine_v3_to_v4.py
index 97a3ae5ed..382074a47 100644
--- a/floris/tools/convert_turbine_v3_to_v4.py
+++ b/floris/tools/convert_turbine_v3_to_v4.py
@@ -26,7 +26,7 @@
 import sys
 from pathlib import Path
 
-from floris.tools import build_turbine_dict, check_smooth_power_curve
+from floris.simulation.turbine import build_cosine_loss_turbine_dict, check_smooth_power_curve
 from floris.utilities import load_yaml
 
 
@@ -71,14 +71,17 @@
         turbine_properties["ref_tilt"] = v3_turbine_dict["ref_tilt_cp_ct"]
 
     # Convert to v4 and print new yaml
-    v4_turbine_dict = build_turbine_dict(
+    v4_turbine_dict = build_cosine_loss_turbine_dict(
         power_thrust_table,
         v3_turbine_dict["turbine_type"],
         output_path,
         **turbine_properties
     )
 
-    if not check_smooth_power_curve(v4_turbine_dict["power_thrust_table"]["power"], tolerance=0.001):
+    if not check_smooth_power_curve(
+        v4_turbine_dict["power_thrust_table"]["power"],
+        tolerance=0.001
+    ):
         print(
             "Non-smoothness detected in output power curve. ",
             "Check above-rated power in generated v4 yaml file."
diff --git a/floris/tools/floris_interface.py b/floris/tools/floris_interface.py
index 07e2eeb71..ef5b992b0 100644
--- a/floris/tools/floris_interface.py
+++ b/floris/tools/floris_interface.py
@@ -22,14 +22,12 @@
 
 from floris.logging_manager import LoggingManager
 from floris.simulation import Floris, State
-from floris.simulation.turbine import (
-    average_velocity,
+from floris.simulation.rotor_velocity import average_velocity
+from floris.simulation.turbine.turbine import (
     axial_induction,
-    Ct,
     power,
-    rotor_effective_velocity,
+    thrust_coefficient,
 )
-from floris.simulation.turbine_multi_dim import multidim_power_down_select, power_multidim
 from floris.tools.cut_plane import CutPlane
 from floris.type_dec import NDArrayFloat
 
@@ -601,74 +599,52 @@ def get_turbine_powers(self) -> NDArrayFloat:
             )
         # Check for negative velocities, which could indicate bad model
         # parameters or turbines very closely spaced.
-        if (self.turbine_effective_velocities < 0.).any():
-            self.logger.warning("Some rotor effective velocities are negative.")
+        if (self.floris.flow_field.u < 0.).any():
+            self.logger.warning("Some velocities at the rotor are negative.")
 
         turbine_powers = power(
-            rotor_effective_velocities=self.turbine_effective_velocities,
-            power_interp=self.floris.farm.turbine_power_interps,
+            velocities=self.floris.flow_field.u,
+            air_density=self.floris.flow_field.air_density,
+            power_functions=self.floris.farm.turbine_power_functions,
+            yaw_angles=self.floris.farm.yaw_angles,
+            tilt_angles=self.floris.farm.tilt_angles,
+            tilt_interps=self.floris.farm.turbine_tilt_interps,
             turbine_type_map=self.floris.farm.turbine_type_map,
+            turbine_power_thrust_tables=self.floris.farm.turbine_power_thrust_tables,
+            correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt,
+            multidim_condition=self.floris.flow_field.multidim_conditions
         )
         return turbine_powers
 
-    def get_turbine_powers_multidim(self) -> NDArrayFloat:
-        """Calculates the power at each turbine in the wind farm
-        when using multi-dimensional Cp/Ct turbine definitions.
-
-        Returns:
-            NDArrayFloat: Powers at each turbine.
-        """
-
-        # Confirm calculate wake has been run
-        if self.floris.state is not State.USED:
-            raise RuntimeError(
-                "Can't run function `FlorisInterface.get_turbine_powers_multidim` without "
-                "first running `FlorisInterface.calculate_wake`."
-            )
-        # Check for negative velocities, which could indicate bad model
-        # parameters or turbines very closely spaced.
-        if (self.turbine_effective_velocities < 0.).any():
-            self.logger.warning("Some rotor effective velocities are negative.")
-
-        turbine_power_interps = multidim_power_down_select(
-            self.floris.farm.turbine_power_interps,
-            self.floris.flow_field.multidim_conditions
-        )
-
-        turbine_powers = power_multidim(
-            ref_air_density=self.floris.farm.ref_air_densities,
-            rotor_effective_velocities=self.turbine_effective_velocities,
-            power_interp=turbine_power_interps,
-        )
-        return turbine_powers
-
-    def get_turbine_Cts(self) -> NDArrayFloat:
-        turbine_Cts = Ct(
+    def get_turbine_thrust_coefficients(self) -> NDArrayFloat:
+        turbine_thrust_coefficients = thrust_coefficient(
             velocities=self.floris.flow_field.u,
-            yaw_angle=self.floris.farm.yaw_angles,
-            tilt_angle=self.floris.farm.tilt_angles,
-            ref_tilt=self.floris.farm.ref_tilts,
-            fCt=self.floris.farm.turbine_fCts,
-            tilt_interp=self.floris.farm.turbine_tilt_interps,
+            yaw_angles=self.floris.farm.yaw_angles,
+            tilt_angles=self.floris.farm.tilt_angles,
+            thrust_coefficient_functions=self.floris.farm.turbine_thrust_coefficient_functions,
+            tilt_interps=self.floris.farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt,
             turbine_type_map=self.floris.farm.turbine_type_map,
+            turbine_power_thrust_tables=self.floris.farm.turbine_power_thrust_tables,
             average_method=self.floris.grid.average_method,
             cubature_weights=self.floris.grid.cubature_weights,
+            multidim_condition=self.floris.flow_field.multidim_conditions
         )
-        return turbine_Cts
+        return turbine_thrust_coefficients
 
     def get_turbine_ais(self) -> NDArrayFloat:
         turbine_ais = axial_induction(
             velocities=self.floris.flow_field.u,
-            yaw_angle=self.floris.farm.yaw_angles,
-            tilt_angle=self.floris.farm.tilt_angles,
-            ref_tilt=self.floris.farm.ref_tilts,
-            fCt=self.floris.farm.turbine_fCts,
-            tilt_interp=self.floris.farm.turbine_tilt_interps,
+            yaw_angles=self.floris.farm.yaw_angles,
+            tilt_angles=self.floris.farm.tilt_angles,
+            axial_induction_functions=self.floris.farm.turbine_axial_induction_functions,
+            tilt_interps=self.floris.farm.turbine_tilt_interps,
             correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt,
             turbine_type_map=self.floris.farm.turbine_type_map,
+            turbine_power_thrust_tables=self.floris.farm.turbine_power_thrust_tables,
             average_method=self.floris.grid.average_method,
             cubature_weights=self.floris.grid.cubature_weights,
+            multidim_condition=self.floris.flow_field.multidim_conditions
         )
         return turbine_ais
 
@@ -680,25 +656,6 @@ def turbine_average_velocities(self) -> NDArrayFloat:
             cubature_weights=self.floris.grid.cubature_weights
         )
 
-    @property
-    def turbine_effective_velocities(self) -> NDArrayFloat:
-        rotor_effective_velocities = rotor_effective_velocity(
-            air_density=self.floris.flow_field.air_density,
-            ref_air_density=self.floris.farm.ref_air_densities,
-            velocities=self.floris.flow_field.u,
-            yaw_angle=self.floris.farm.yaw_angles,
-            tilt_angle=self.floris.farm.tilt_angles,
-            ref_tilt=self.floris.farm.ref_tilts,
-            pP=self.floris.farm.pPs,
-            pT=self.floris.farm.pTs,
-            tilt_interp=self.floris.farm.turbine_tilt_interps,
-            correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt,
-            turbine_type_map=self.floris.farm.turbine_type_map,
-            average_method=self.floris.grid.average_method,
-            cubature_weights=self.floris.grid.cubature_weights
-        )
-        return rotor_effective_velocities
-
     def get_turbine_TIs(self) -> NDArrayFloat:
         return self.floris.flow_field.turbulence_intensity_field
 
diff --git a/floris/tools/uncertainty_interface.py b/floris/tools/uncertainty_interface.py
index b871bd86d..7f2b833ef 100644
--- a/floris/tools/uncertainty_interface.py
+++ b/floris/tools/uncertainty_interface.py
@@ -627,8 +627,8 @@ def assign_hub_height_to_ref_height(self):
     def get_turbine_layout(self, z=False):
         return self.fi.get_turbine_layout(z=z)
 
-    def get_turbine_Cts(self):
-        return self.fi.get_turbine_Cts()
+    def get_turbine_thrust_coefficients(self):
+        return self.fi.get_turbine_thrust_coefficients()
 
     def get_turbine_ais(self):
         return self.fi.get_turbine_ais()
diff --git a/floris/turbine_library/__init__.py b/floris/turbine_library/__init__.py
index 828c50eb2..42e1962f3 100644
--- a/floris/turbine_library/__init__.py
+++ b/floris/turbine_library/__init__.py
@@ -1 +1,5 @@
 from floris.turbine_library.turbine_previewer import TurbineInterface, TurbineLibrary
+from floris.turbine_library.turbine_utilities import (
+    build_cosine_loss_turbine_dict,
+    check_smooth_power_curve,
+)
diff --git a/floris/turbine_library/iea_10MW.yaml b/floris/turbine_library/iea_10MW.yaml
index eaa04d81b..daa58256d 100644
--- a/floris/turbine_library/iea_10MW.yaml
+++ b/floris/turbine_library/iea_10MW.yaml
@@ -1,178 +1,179 @@
-turbine_type: 'iea_10MW'
+turbine_type: iea_10MW
 generator_efficiency: 1.0
 hub_height: 119.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 198.0
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 6.0
+power_thrust_model: cosine-loss
 power_thrust_table:
-  power:
-    - 0.000000
-    - 0.000000
-    - 0.074
-    - 0.325100
-    - 0.376200
-    - 0.402700
-    - 0.415600
-    - 0.423000
-    - 0.427400
-    - 0.429300
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.429800
-    - 0.430500
-    - 0.438256
-    - 0.425908
-    - 0.347037
-    - 0.307306
-    - 0.271523
-    - 0.239552
-    - 0.211166
-    - 0.186093
-    - 0.164033
-    - 0.144688
-    - 0.127760
-    - 0.112969
-    - 0.100062
-    - 0.088800
-    - 0.078975
-    - 0.070401
-    - 0.062913
-    - 0.056368
-    - 0.050640
-    - 0.045620
-    - 0.041216
-    - 0.037344
-    - 0.033935
-    - 0.0
-    - 0.0
-  thrust:
-    - 0.0
-    - 0.0
-    - 0.7701
-    - 0.7701
-    - 0.7763
-    - 0.7824
-    - 0.7820
-    - 0.7802
-    - 0.7772
-    - 0.7719
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7768
-    - 0.7675
-    - 0.7651
-    - 0.7587
-    - 0.5056
-    - 0.4310
-    - 0.3708
-    - 0.3209
-    - 0.2788
-    - 0.2432
-    - 0.2128
-    - 0.1868
-    - 0.1645
-    - 0.1454
-    - 0.1289
-    - 0.1147
-    - 0.1024
-    - 0.0918
-    - 0.0825
-    - 0.0745
-    - 0.0675
-    - 0.0613
-    - 0.0559
-    - 0.0512
-    - 0.0470
-    - 0.0
-    - 0.0
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
   wind_speed:
-    - 0.0000
-    - 2.9
-    - 3.0
-    - 4.0000
-    - 4.5147
-    - 5.0008
-    - 5.4574
-    - 5.8833
-    - 6.2777
-    - 6.6397
-    - 6.9684
-    - 7.2632
-    - 7.5234
-    - 7.7484
-    - 7.9377
-    - 8.0909
-    - 8.2077
-    - 8.2877
-    - 8.3308
-    - 8.3370
-    - 8.3678
-    - 8.4356
-    - 8.5401
-    - 8.6812
-    - 8.8585
-    - 9.0717
-    - 9.3202
-    - 9.6035
-    - 9.9210
-    - 10.2720
-    - 10.6557
-    - 10.7577
-    - 11.5177
-    - 11.9941
-    - 12.4994
-    - 13.0324
-    - 13.5920
-    - 14.1769
-    - 14.7859
-    - 15.4175
-    - 16.0704
-    - 16.7432
-    - 17.4342
-    - 18.1421
-    - 18.8652
-    - 19.6019
-    - 20.3506
-    - 21.1096
-    - 21.8773
-    - 22.6519
-    - 23.4317
-    - 24.2150
-    - 25.010
-    - 25.020
-    - 50.0
+  - 0.0
+  - 2.9
+  - 3.0
+  - 4.0
+  - 4.5147
+  - 5.0008
+  - 5.4574
+  - 5.8833
+  - 6.2777
+  - 6.6397
+  - 6.9684
+  - 7.2632
+  - 7.5234
+  - 7.7484
+  - 7.9377
+  - 8.0909
+  - 8.2077
+  - 8.2877
+  - 8.3308
+  - 8.337
+  - 8.3678
+  - 8.4356
+  - 8.5401
+  - 8.6812
+  - 8.8585
+  - 9.0717
+  - 9.3202
+  - 9.6035
+  - 9.921
+  - 10.272
+  - 10.6557
+  - 10.7577
+  - 11.5177
+  - 11.9941
+  - 12.4994
+  - 13.0324
+  - 13.592
+  - 14.1769
+  - 14.7859
+  - 15.4175
+  - 16.0704
+  - 16.7432
+  - 17.4342
+  - 18.1421
+  - 18.8652
+  - 19.6019
+  - 20.3506
+  - 21.1096
+  - 21.8773
+  - 22.6519
+  - 23.4317
+  - 24.215
+  - 25.01
+  - 25.02
+  - 50.0
+  power:
+  - 0.0
+  - 0.0
+  - 37.68094958908877
+  - 392.3948496148231
+  - 652.8777029978363
+  - 949.7874838458624
+  - 1273.9701534366477
+  - 1624.53736790407
+  - 1994.1716868646631
+  - 2369.9141552410333
+  - 2742.7863681556505
+  - 3105.823526184341
+  - 3451.7173408365657
+  - 3770.7597566998656
+  - 4053.935262364495
+  - 4293.221213633668
+  - 4481.848670501228
+  - 4614.183183672742
+  - 4686.546075837561
+  - 4697.017416780224
+  - 4749.267597733971
+  - 4865.648149450861
+  - 5048.724054152798
+  - 5303.127287084259
+  - 5634.732904516438
+  - 6051.44102592321
+  - 6562.487084906048
+  - 7179.28820897481
+  - 7915.149369234113
+  - 8799.632659018345
+  - 10000.004148840422
+  - 10000.010118342427
+  - 9999.986697903953
+  - 10000.00900096281
+  - 10000.010994188466
+  - 9999.985254153351
+  - 10000.01026748458
+  - 10000.005066662203
+  - 10000.02018584477
+  - 10000.017032649757
+  - 10000.030351494535
+  - 10000.023814906699
+  - 10000.036965698706
+  - 10000.045823704839
+  - 10000.005313131529
+  - 9999.992881648563
+  - 9999.96325689038
+  - 9999.976811614484
+  - 10000.028061758208
+  - 9999.89737385537
+  - 10000.082694480527
+  - 10000.014032855759
+  - 10011.87188590296
+  - 0.0
+  - 0.0
+  thrust_coefficient:
+  - 0.0
+  - 0.0
+  - 0.7701
+  - 0.7701
+  - 0.7763
+  - 0.7824
+  - 0.782
+  - 0.7802
+  - 0.7772
+  - 0.7719
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7768
+  - 0.7675
+  - 0.7651
+  - 0.7587
+  - 0.5056
+  - 0.431
+  - 0.3708
+  - 0.3209
+  - 0.2788
+  - 0.2432
+  - 0.2128
+  - 0.1868
+  - 0.1645
+  - 0.1454
+  - 0.1289
+  - 0.1147
+  - 0.1024
+  - 0.0918
+  - 0.0825
+  - 0.0745
+  - 0.0675
+  - 0.0613
+  - 0.0559
+  - 0.0512
+  - 0.047
+  - 0.0
+  - 0.0
diff --git a/floris/turbine_library/iea_10MW_v3legacy.yaml b/floris/turbine_library/iea_10MW_v3legacy.yaml
new file mode 100644
index 000000000..eaa04d81b
--- /dev/null
+++ b/floris/turbine_library/iea_10MW_v3legacy.yaml
@@ -0,0 +1,178 @@
+turbine_type: 'iea_10MW'
+generator_efficiency: 1.0
+hub_height: 119.0
+pP: 1.88
+pT: 1.88
+rotor_diameter: 198.0
+TSR: 8.0
+ref_density_cp_ct: 1.225
+ref_tilt_cp_ct: 6.0
+power_thrust_table:
+  power:
+    - 0.000000
+    - 0.000000
+    - 0.074
+    - 0.325100
+    - 0.376200
+    - 0.402700
+    - 0.415600
+    - 0.423000
+    - 0.427400
+    - 0.429300
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.429800
+    - 0.430500
+    - 0.438256
+    - 0.425908
+    - 0.347037
+    - 0.307306
+    - 0.271523
+    - 0.239552
+    - 0.211166
+    - 0.186093
+    - 0.164033
+    - 0.144688
+    - 0.127760
+    - 0.112969
+    - 0.100062
+    - 0.088800
+    - 0.078975
+    - 0.070401
+    - 0.062913
+    - 0.056368
+    - 0.050640
+    - 0.045620
+    - 0.041216
+    - 0.037344
+    - 0.033935
+    - 0.0
+    - 0.0
+  thrust:
+    - 0.0
+    - 0.0
+    - 0.7701
+    - 0.7701
+    - 0.7763
+    - 0.7824
+    - 0.7820
+    - 0.7802
+    - 0.7772
+    - 0.7719
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7768
+    - 0.7675
+    - 0.7651
+    - 0.7587
+    - 0.5056
+    - 0.4310
+    - 0.3708
+    - 0.3209
+    - 0.2788
+    - 0.2432
+    - 0.2128
+    - 0.1868
+    - 0.1645
+    - 0.1454
+    - 0.1289
+    - 0.1147
+    - 0.1024
+    - 0.0918
+    - 0.0825
+    - 0.0745
+    - 0.0675
+    - 0.0613
+    - 0.0559
+    - 0.0512
+    - 0.0470
+    - 0.0
+    - 0.0
+  wind_speed:
+    - 0.0000
+    - 2.9
+    - 3.0
+    - 4.0000
+    - 4.5147
+    - 5.0008
+    - 5.4574
+    - 5.8833
+    - 6.2777
+    - 6.6397
+    - 6.9684
+    - 7.2632
+    - 7.5234
+    - 7.7484
+    - 7.9377
+    - 8.0909
+    - 8.2077
+    - 8.2877
+    - 8.3308
+    - 8.3370
+    - 8.3678
+    - 8.4356
+    - 8.5401
+    - 8.6812
+    - 8.8585
+    - 9.0717
+    - 9.3202
+    - 9.6035
+    - 9.9210
+    - 10.2720
+    - 10.6557
+    - 10.7577
+    - 11.5177
+    - 11.9941
+    - 12.4994
+    - 13.0324
+    - 13.5920
+    - 14.1769
+    - 14.7859
+    - 15.4175
+    - 16.0704
+    - 16.7432
+    - 17.4342
+    - 18.1421
+    - 18.8652
+    - 19.6019
+    - 20.3506
+    - 21.1096
+    - 21.8773
+    - 22.6519
+    - 23.4317
+    - 24.2150
+    - 25.010
+    - 25.020
+    - 50.0
diff --git a/floris/turbine_library/iea_10MW_v4converted.yaml b/floris/turbine_library/iea_10MW_v4converted.yaml
index 7258b388b..daa58256d 100644
--- a/floris/turbine_library/iea_10MW_v4converted.yaml
+++ b/floris/turbine_library/iea_10MW_v4converted.yaml
@@ -1,13 +1,14 @@
 turbine_type: iea_10MW
 generator_efficiency: 1.0
 hub_height: 119.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 198.0
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 6.0
+power_thrust_model: cosine-loss
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
   wind_speed:
   - 0.0
   - 2.9
diff --git a/floris/turbine_library/iea_10MW_v4updated.yaml b/floris/turbine_library/iea_10MW_v4updated.yaml
index 9328982ba..ae745b46b 100644
--- a/floris/turbine_library/iea_10MW_v4updated.yaml
+++ b/floris/turbine_library/iea_10MW_v4updated.yaml
@@ -3,13 +3,13 @@
 turbine_type: 'iea_10MW'
 generator_efficiency: 1.0
 hub_height: 119.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 198.0
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 6.0
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.000000
     - 0.000000
diff --git a/floris/turbine_library/iea_15MW.yaml b/floris/turbine_library/iea_15MW.yaml
index 0350cd9c4..d1f93dc4b 100644
--- a/floris/turbine_library/iea_15MW.yaml
+++ b/floris/turbine_library/iea_15MW.yaml
@@ -1,172 +1,173 @@
-turbine_type: 'iea_15MW'
+turbine_type: iea_15MW
 generator_efficiency: 1.0
 hub_height: 150.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 242.24
 TSR: 8.0
-ref_density_cp_ct: 1.225
-ref_tilt_cp_ct: 6.0
+power_thrust_model: cosine-loss
 power_thrust_table:
-  power:
-    - 0.000000
-    - 0.049361236
-    - 0.224324252
-    - 0.312216418
-    - 0.36009987
-    - 0.38761204
-    - 0.404010164
-    - 0.413979324
-    - 0.420083692
-    - 0.423787764
-    - 0.425977895
-    - 0.427193272
-    - 0.427183505
-    - 0.426860928
-    - 0.426617959
-    - 0.426458783
-    - 0.426385957
-    - 0.426371389
-    - 0.426268826
-    - 0.426077456
-    - 0.425795302
-    - 0.425420049
-    - 0.424948854
-    - 0.424379028
-    - 0.423707714
-    - 0.422932811
-    - 0.422052556
-    - 0.421065815
-    - 0.419972455
-    - 0.419400676
-    - 0.418981957
-    - 0.385839135
-    - 0.335840083
-    - 0.29191329
-    - 0.253572514
-    - 0.220278082
-    - 0.191477908
-    - 0.166631343
-    - 0.145236797
-    - 0.126834289
-    - 0.111011925
-    - 0.097406118
-    - 0.085699408
-    - 0.075616912
-    - 0.066922115
-    - 0.059412477
-    - 0.052915227
-    - 0.04728299
-    - 0.042390922
-    - 0.038132739
-    - 0.03441828
-    - 0.0
-    - 0.0
-  thrust:
-    - 0.000000
-    - 0.817533319
-    - 0.792115292
-    - 0.786401899
-    - 0.788898744
-    - 0.790774576
-    - 0.79208669
-    - 0.79185809
-    - 0.7903853
-    - 0.788253035
-    - 0.785845184
-    - 0.783367164
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.77853469
-    - 0.781531069
-    - 0.758935311
-    - 0.614478855
-    - 0.498687801
-    - 0.416354609
-    - 0.351944846
-    - 0.299832337
-    - 0.256956606
-    - 0.221322169
-    - 0.19150758
-    - 0.166435523
-    - 0.145263684
-    - 0.127319849
-    - 0.11206048
-    - 0.099042189
-    - 0.087901155
-    - 0.078337446
-    - 0.07010295
-    - 0.062991402
-    - 0.056831647
-    - 0.05148062
-    - 0.046818787
-    - 0.0
-    - 0.0
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
   wind_speed:
-    - 0.000
-    - 3
-    - 3.54953237
-    - 4.067900771
-    - 4.553906848
-    - 5.006427063
-    - 5.424415288
-    - 5.806905228
-    - 6.153012649
-    - 6.461937428
-    - 6.732965398
-    - 6.965470002
-    - 7.158913742
-    - 7.312849418
-    - 7.426921164
-    - 7.500865272
-    - 7.534510799
-    - 7.541241633
-    - 7.58833327
-    - 7.675676842
-    - 7.803070431
-    - 7.970219531
-    - 8.176737731
-    - 8.422147605
-    - 8.70588182
-    - 9.027284445
-    - 9.385612468
-    - 9.780037514
-    - 10.20964776
-    - 10.67345004
-    - 10.86770694
-    - 11.17037214
-    - 11.6992653
-    - 12.25890683
-    - 12.84800295
-    - 13.46519181
-    - 14.10904661
-    - 14.77807889
-    - 15.470742
-    - 16.18543466
-    - 16.92050464
-    - 17.67425264
-    - 18.44493615
-    - 19.23077353
-    - 20.02994808
-    - 20.8406123
-    - 21.66089211
-    - 22.4888912
-    - 23.32269542
-    - 24.1603772
-    - 25
-    - 25.020
-    - 50.0
+  - 0.0
+  - 3.0
+  - 3.54953237
+  - 4.067900771
+  - 4.553906848
+  - 5.006427063
+  - 5.424415288
+  - 5.806905228
+  - 6.153012649
+  - 6.461937428
+  - 6.732965398
+  - 6.965470002
+  - 7.158913742
+  - 7.312849418
+  - 7.426921164
+  - 7.500865272
+  - 7.534510799
+  - 7.541241633
+  - 7.58833327
+  - 7.675676842
+  - 7.803070431
+  - 7.970219531
+  - 8.176737731
+  - 8.422147605
+  - 8.70588182
+  - 9.027284445
+  - 9.385612468
+  - 9.780037514
+  - 10.20964776
+  - 10.67345004
+  - 10.86770694
+  - 11.17037214
+  - 11.6992653
+  - 12.25890683
+  - 12.84800295
+  - 13.46519181
+  - 14.10904661
+  - 14.77807889
+  - 15.470742
+  - 16.18543466
+  - 16.92050464
+  - 17.67425264
+  - 18.44493615
+  - 19.23077353
+  - 20.02994808
+  - 20.8406123
+  - 21.66089211
+  - 22.4888912
+  - 23.32269542
+  - 24.1603772
+  - 25.0
+  - 25.02
+  - 50.0
+  power:
+  - 0.0
+  - 37.62161892251866
+  - 283.1896270728138
+  - 593.2728560522313
+  - 959.9819840653767
+  - 1372.9939673445779
+  - 1820.2824213031413
+  - 2288.234638675552
+  - 2762.402356940621
+  - 3227.9317849259483
+  - 3670.23524006855
+  - 4075.3355492549404
+  - 4424.289670276729
+  - 4712.31145096999
+  - 4933.478791318434
+  - 5080.411002639729
+  - 5148.20416793432
+  - 5161.8373266616445
+  - 5257.877358155053
+  - 5439.0905873988
+  - 5710.644642926693
+  - 6080.1808123220335
+  - 6557.896472825747
+  - 7156.656114121487
+  - 7892.096068144686
+  - 8782.7485712001
+  - 9850.132658272489
+  - 11118.833728910668
+  - 12616.55466282621
+  - 14395.650060011094
+  - 15180.873696159935
+  - 15180.878025972781
+  - 15180.846427684693
+  - 15180.874525641515
+  - 15180.873081482694
+  - 15180.868180147516
+  - 15180.964634095619
+  - 15180.928211309449
+  - 15180.909227363609
+  - 15180.898248776428
+  - 15180.890850809097
+  - 15180.885382324133
+  - 15180.881159484874
+  - 15180.877937975014
+  - 15180.875500759283
+  - 15180.873891022644
+  - 15180.894816053498
+  - 15180.873173416821
+  - 15180.873965755092
+  - 15180.875620174738
+  - 15180.87762584068
+  - 0.0
+  - 0.0
+  thrust_coefficient:
+  - 0.0
+  - 0.817533319
+  - 0.792115292
+  - 0.786401899
+  - 0.788898744
+  - 0.790774576
+  - 0.79208669
+  - 0.79185809
+  - 0.7903853
+  - 0.788253035
+  - 0.785845184
+  - 0.783367164
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.77853469
+  - 0.781531069
+  - 0.758935311
+  - 0.614478855
+  - 0.498687801
+  - 0.416354609
+  - 0.351944846
+  - 0.299832337
+  - 0.256956606
+  - 0.221322169
+  - 0.19150758
+  - 0.166435523
+  - 0.145263684
+  - 0.127319849
+  - 0.11206048
+  - 0.099042189
+  - 0.087901155
+  - 0.078337446
+  - 0.07010295
+  - 0.062991402
+  - 0.056831647
+  - 0.05148062
+  - 0.046818787
+  - 0.0
+  - 0.0
diff --git a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml
index efac909cb..127923ae4 100644
--- a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml
+++ b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml
@@ -1,14 +1,15 @@
 turbine_type: 'iea_15MW_floating'
 generator_efficiency: 1.0
 hub_height: 150.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 242.24
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 6.0
 multi_dimensional_cp_ct: True
-power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
+power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
+  power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
 floating_tilt_table:
   tilt:
     - 5.747296314800103
diff --git a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct_v3legacy.yaml b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct_v3legacy.yaml
new file mode 100644
index 000000000..58b2b3a1f
--- /dev/null
+++ b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct_v3legacy.yaml
@@ -0,0 +1,29 @@
+turbine_type: 'iea_15MW_floating'
+generator_efficiency: 1.0
+hub_height: 150.0
+pP: 1.88
+pT: 1.88
+rotor_diameter: 242.24
+TSR: 8.0
+ref_density_cp_ct: 1.225
+ref_tilt_cp_ct: 6.0
+multi_dimensional_cp_ct: True
+power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
+floating_tilt_table:
+  tilt:
+    - 5.747296314800103
+    - 7.2342400188651068
+    - 9.0468701999352397
+    - 9.762182013267733
+    - 8.795649572299896
+    - 8.089078308325314
+    - 7.7229584934943614
+  wind_speed:
+    - 4.0
+    - 6.0
+    - 8.0
+    - 10.0
+    - 12.0
+    - 14.0
+    - 16.0
+correct_cp_ct_for_tilt: True
diff --git a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml
index 139bd45e0..756f3dc1d 100644
--- a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml
+++ b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml
@@ -1,11 +1,12 @@
 turbine_type: 'iea_15MW_multi_dim_cp_ct'
 generator_efficiency: 1.0
 hub_height: 150.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 242.24
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 6.0
 multi_dimensional_cp_ct: True
-power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
+power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
+  power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv'
diff --git a/floris/turbine_library/iea_15MW_v3legacy.yaml b/floris/turbine_library/iea_15MW_v3legacy.yaml
new file mode 100644
index 000000000..0350cd9c4
--- /dev/null
+++ b/floris/turbine_library/iea_15MW_v3legacy.yaml
@@ -0,0 +1,172 @@
+turbine_type: 'iea_15MW'
+generator_efficiency: 1.0
+hub_height: 150.0
+pP: 1.88
+pT: 1.88
+rotor_diameter: 242.24
+TSR: 8.0
+ref_density_cp_ct: 1.225
+ref_tilt_cp_ct: 6.0
+power_thrust_table:
+  power:
+    - 0.000000
+    - 0.049361236
+    - 0.224324252
+    - 0.312216418
+    - 0.36009987
+    - 0.38761204
+    - 0.404010164
+    - 0.413979324
+    - 0.420083692
+    - 0.423787764
+    - 0.425977895
+    - 0.427193272
+    - 0.427183505
+    - 0.426860928
+    - 0.426617959
+    - 0.426458783
+    - 0.426385957
+    - 0.426371389
+    - 0.426268826
+    - 0.426077456
+    - 0.425795302
+    - 0.425420049
+    - 0.424948854
+    - 0.424379028
+    - 0.423707714
+    - 0.422932811
+    - 0.422052556
+    - 0.421065815
+    - 0.419972455
+    - 0.419400676
+    - 0.418981957
+    - 0.385839135
+    - 0.335840083
+    - 0.29191329
+    - 0.253572514
+    - 0.220278082
+    - 0.191477908
+    - 0.166631343
+    - 0.145236797
+    - 0.126834289
+    - 0.111011925
+    - 0.097406118
+    - 0.085699408
+    - 0.075616912
+    - 0.066922115
+    - 0.059412477
+    - 0.052915227
+    - 0.04728299
+    - 0.042390922
+    - 0.038132739
+    - 0.03441828
+    - 0.0
+    - 0.0
+  thrust:
+    - 0.000000
+    - 0.817533319
+    - 0.792115292
+    - 0.786401899
+    - 0.788898744
+    - 0.790774576
+    - 0.79208669
+    - 0.79185809
+    - 0.7903853
+    - 0.788253035
+    - 0.785845184
+    - 0.783367164
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.77853469
+    - 0.781531069
+    - 0.758935311
+    - 0.614478855
+    - 0.498687801
+    - 0.416354609
+    - 0.351944846
+    - 0.299832337
+    - 0.256956606
+    - 0.221322169
+    - 0.19150758
+    - 0.166435523
+    - 0.145263684
+    - 0.127319849
+    - 0.11206048
+    - 0.099042189
+    - 0.087901155
+    - 0.078337446
+    - 0.07010295
+    - 0.062991402
+    - 0.056831647
+    - 0.05148062
+    - 0.046818787
+    - 0.0
+    - 0.0
+  wind_speed:
+    - 0.000
+    - 3
+    - 3.54953237
+    - 4.067900771
+    - 4.553906848
+    - 5.006427063
+    - 5.424415288
+    - 5.806905228
+    - 6.153012649
+    - 6.461937428
+    - 6.732965398
+    - 6.965470002
+    - 7.158913742
+    - 7.312849418
+    - 7.426921164
+    - 7.500865272
+    - 7.534510799
+    - 7.541241633
+    - 7.58833327
+    - 7.675676842
+    - 7.803070431
+    - 7.970219531
+    - 8.176737731
+    - 8.422147605
+    - 8.70588182
+    - 9.027284445
+    - 9.385612468
+    - 9.780037514
+    - 10.20964776
+    - 10.67345004
+    - 10.86770694
+    - 11.17037214
+    - 11.6992653
+    - 12.25890683
+    - 12.84800295
+    - 13.46519181
+    - 14.10904661
+    - 14.77807889
+    - 15.470742
+    - 16.18543466
+    - 16.92050464
+    - 17.67425264
+    - 18.44493615
+    - 19.23077353
+    - 20.02994808
+    - 20.8406123
+    - 21.66089211
+    - 22.4888912
+    - 23.32269542
+    - 24.1603772
+    - 25
+    - 25.020
+    - 50.0
diff --git a/floris/turbine_library/iea_15MW_v4converted.yaml b/floris/turbine_library/iea_15MW_v4converted.yaml
index 66a7161cc..d1f93dc4b 100644
--- a/floris/turbine_library/iea_15MW_v4converted.yaml
+++ b/floris/turbine_library/iea_15MW_v4converted.yaml
@@ -1,13 +1,14 @@
 turbine_type: iea_15MW
 generator_efficiency: 1.0
 hub_height: 150.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 242.24
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 6.0
+power_thrust_model: cosine-loss
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
   wind_speed:
   - 0.0
   - 3.0
diff --git a/floris/turbine_library/iea_15MW_v4updated.yaml b/floris/turbine_library/iea_15MW_v4updated.yaml
index 45d48b525..163a3da74 100644
--- a/floris/turbine_library/iea_15MW_v4updated.yaml
+++ b/floris/turbine_library/iea_15MW_v4updated.yaml
@@ -4,13 +4,13 @@
 turbine_type: 'iea_15MW'
 generator_efficiency: 1.0
 hub_height: 150.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 242.24
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 6.0
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 6.0
+  pP: 1.88
+  pT: 1.88
   power:
     - 0.000000
     - 0.000000
diff --git a/floris/turbine_library/nrel_5MW.yaml b/floris/turbine_library/nrel_5MW.yaml
index 4a202645c..4337ac8f7 100644
--- a/floris/turbine_library/nrel_5MW.yaml
+++ b/floris/turbine_library/nrel_5MW.yaml
@@ -17,14 +17,6 @@ generator_efficiency: 1.0
 # Hub height.
 hub_height: 90.0
 
-###
-# Cosine exponent for power loss due to yaw misalignment.
-pP: 1.88
-
-###
-# Cosine exponent for power loss due to tilt.
-pT: 1.88
-
 ###
 # Rotor diameter.
 rotor_diameter: 126.0
@@ -34,155 +26,179 @@ rotor_diameter: 126.0
 TSR: 8.0
 
 ###
-# The air density at which the Cp and Ct curves are defined.
-ref_air_density: 1.225
-
-###
-# The tilt angle at which the Cp and Ct curves are defined. This is used to capture
-# the effects of a floating platform on a turbine's power and wake.
-ref_tilt: 5.0
+# Model for power and thrust curve interpretation.
+power_thrust_model: 'cosine-loss'
 
 ###
 # Cp and Ct as a function of wind speed for the turbine's full range of operating conditions.
 power_thrust_table:
-  power:
-    - 0.0
-    - 0.0
-    - 40.5
-    - 177.7
-    - 403.9
-    - 737.6
-    - 1187.2
-    - 1771.1
-    - 2518.6
-    - 3448.41
-    - 3552.15
-    - 3657.95
-    - 3765.16
-    - 3873.95
-    - 3984.49
-    - 4096.56
-    - 4210.69
-    - 4326.15
-    - 4443.41
-    - 4562.51
-    - 4683.43
-    - 4806.18
-    - 4929.92
-    - 5000.37
-    - 5000.02
-    - 5000.0
-    - 4999.99
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 5000.0
-    - 0.0
-    - 0.0
-  thrust_coefficient:
-    - 0.0
-    - 0.0
-    - 2.497990147
-    - 1.766833378
-    - 1.408360153
-    - 1.201348494
-    - 1.065133759
-    - 0.977936955
-    - 0.936281559
-    - 0.905425262
-    - 0.902755344
-    - 0.90016155
-    - 0.895745235
-    - 0.889630636
-    - 0.883651878
-    - 0.877788261
-    - 0.872068513
-    - 0.866439424
-    - 0.860930874
-    - 0.855544522
-    - 0.850276473
-    - 0.845148048
-    - 0.840105118
-    - 0.811165614
-    - 0.764009698
-    - 0.728584172
-    - 0.698944675
-    - 0.672754103
-    - 0.649082557
-    - 0.627368152
-    - 0.471373796
-    - 0.372703289
-    - 0.30290131
-    - 0.251235686
-    - 0.211900735
-    - 0.181210571
-    - 0.156798163
-    - 0.137091212
-    - 0.120753164
-    - 0.106941036
-    - 0.095319286
-    - 0.085631997
-    - 0.077368152
-    - 0.0
-    - 0.0
+  ### Power thrust table parameters
+  # The air density at which the Cp and Ct curves are defined.
+  ref_air_density: 1.225
+  # The tilt angle at which the Cp and Ct curves are defined. This is used to capture
+  # the effects of a floating platform on a turbine's power and wake.
+  ref_tilt: 5.0
+  # Cosine exponent for power loss due to tilt.
+  pT: 1.88
+  # Cosine exponent for power loss due to yaw misalignment.
+  pP: 1.88
+  ### Power thrust table data
   wind_speed:
     - 0.0
-    - 2.9
+    - 2.0
+    - 2.5
     - 3.0
+    - 3.5
     - 4.0
+    - 4.5
     - 5.0
+    - 5.5
     - 6.0
+    - 6.5
     - 7.0
+    - 7.5
     - 8.0
+    - 8.5
     - 9.0
+    - 9.5
     - 10.0
-    - 10.1
-    - 10.2
-    - 10.3
-    - 10.4
     - 10.5
-    - 10.6
-    - 10.7
-    - 10.8
-    - 10.9
     - 11.0
-    - 11.1
-    - 11.2
-    - 11.3
-    - 11.4
     - 11.5
-    - 11.6
-    - 11.7
-    - 11.8
-    - 11.9
     - 12.0
+    - 12.5
     - 13.0
+    - 13.5
     - 14.0
+    - 14.5
     - 15.0
+    - 15.5
     - 16.0
+    - 16.5
     - 17.0
+    - 17.5
     - 18.0
+    - 18.5
     - 19.0
+    - 19.5
     - 20.0
+    - 20.5
     - 21.0
+    - 21.5
     - 22.0
+    - 22.5
     - 23.0
+    - 23.5
     - 24.0
+    - 24.5
     - 25.0
     - 25.01
+    - 25.02
     - 50.0
+  power:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 36.722155848902254
+    - 94.65678115354163
+    - 170.596391826316
+    - 267.74933496419163
+    - 387.64681352354114
+    - 533.9617151673435
+    - 707.4062402827329
+    - 909.9965782677073
+    - 1142.7197798534328
+    - 1407.4994184495558
+    - 1707.1272243371227
+    - 2047.3355806543098
+    - 2430.5778091805637
+    - 2858.3081150622215
+    - 3329.100627354195
+    - 3842.9755943182267
+    - 4403.86140594055
+    - 4999.993508066915
+    - 4999.99850473839
+    - 4999.997854617397
+    - 5000.00304890274
+    - 5000.002113339491
+    - 4999.997282778227
+    - 5000.002243172759
+    - 5000.000360590384
+    - 5000.009074693787
+    - 4999.987262704901
+    - 5000.007345811091
+    - 5000.006875165497
+    - 4999.994990648268
+    - 4999.97705933755
+    - 4999.983698972648
+    - 4999.991318085188
+    - 5000.024022703328
+    - 5000.016589748782
+    - 5000.025709581146
+    - 4999.944891236294
+    - 5000.035324880168
+    - 4999.967955734346
+    - 5000.013248451465
+    - 5000.063199891701
+    - 5000.068982245371
+    - 4999.9325188896555
+    - 5000.011035557985
+    - 5000.012771123277
+    - 4717.243379938609
+    - 0.0
+    - 0.0
+  thrust_coefficient:
+   - 0.0
+   - 0.0
+   - 0.0
+   - 0.99
+   - 0.99
+   - 0.97373036
+   - 0.92826162
+   - 0.89210543
+   - 0.86100905
+   - 0.835423
+   - 0.81237673
+   - 0.79225789
+   - 0.77584769
+   - 0.7629228
+   - 0.76156073
+   - 0.76261984
+   - 0.76169723
+   - 0.75232027
+   - 0.74026851
+   - 0.72987175
+   - 0.70701647
+   - 0.54054532
+   - 0.45509459
+   - 0.39343381
+   - 0.34250785
+   - 0.30487242
+   - 0.27164979
+   - 0.24361964
+   - 0.21973831
+   - 0.19918151
+   - 0.18131868
+   - 0.16537679
+   - 0.15103727
+   - 0.13998636
+   - 0.1289037
+   - 0.11970413
+   - 0.11087113
+   - 0.10339901
+   - 0.09617888
+   - 0.09009926
+   - 0.08395078
+   - 0.0791188
+   - 0.07448356
+   - 0.07050731
+   - 0.06684119
+   - 0.06345518
+   - 0.06032267
+   - 0.05741999
+   - 0.05472609
+   - 0.0
+   - 0.0
 
 ###
 # A boolean flag used when the user wants FLORIS to use the user-supplied multi-dimensional
diff --git a/floris/turbine_library/nrel_5MW_v4converted.yaml b/floris/turbine_library/nrel_5MW_v4converted.yaml
index 0dba7d187..0bd7fb08a 100644
--- a/floris/turbine_library/nrel_5MW_v4converted.yaml
+++ b/floris/turbine_library/nrel_5MW_v4converted.yaml
@@ -1,13 +1,14 @@
 turbine_type: nrel_5MW
 generator_efficiency: 1.0
 hub_height: 90.0
-pP: 1.88
-pT: 1.88
 rotor_diameter: 126.0
 TSR: 8.0
-ref_air_density: 1.225
-ref_tilt: 5.0
+power_thrust_model: cosine-loss
 power_thrust_table:
+  ref_air_density: 1.225
+  ref_tilt: 5.0
+  pP: 1.88
+  pT: 1.88
   wind_speed:
   - 0.0
   - 2.0
diff --git a/floris/turbine_library/nrel_5MW_v4updated.yaml b/floris/turbine_library/nrel_5MW_v4updated.yaml
index a2946c690..d12fcf668 100644
--- a/floris/turbine_library/nrel_5MW_v4updated.yaml
+++ b/floris/turbine_library/nrel_5MW_v4updated.yaml
@@ -16,14 +16,6 @@ generator_efficiency: 1.0
 # Hub height.
 hub_height: 90.0
 
-###
-# Cosine exponent for power loss due to yaw misalignment.
-pP: 1.88
-
-###
-# Cosine exponent for power loss due to tilt.
-pT: 1.88
-
 ###
 # Rotor diameter.
 rotor_diameter: 126.0
@@ -32,18 +24,20 @@ rotor_diameter: 126.0
 # Tip speed ratio defined as linear blade tip speed normalized by the incoming wind speed.
 TSR: 8.0
 
-###
-# The air density at which the Cp and Ct curves are defined.
-ref_air_density: 1.225
-
-###
-# The tilt angle at which the Cp and Ct curves are defined. This is used to capture
-# the effects of a floating platform on a turbine's power and wake.
-ref_tilt: 5.0
-
 ###
 # Cp and Ct as a function of wind speed for the turbine's full range of operating conditions.
 power_thrust_table:
+  ### Power thrust table parameters
+  # The air density at which the Cp and Ct curves are defined.
+  ref_air_density: 1.225
+  # The tilt angle at which the Cp and Ct curves are defined. This is used to capture
+  # the effects of a floating platform on a turbine's power and wake.
+  ref_tilt: 5.0
+  # Cosine exponent for power loss due to tilt.
+  pT: 1.88
+  # Cosine exponent for power loss due to yaw misalignment.
+  pP: 1.88
+  ### Power thrust table data
   power:
     - 0.0
     - 0.0
diff --git a/floris/turbine_library/turbine_previewer.py b/floris/turbine_library/turbine_previewer.py
index 2c624a559..f8f584448 100644
--- a/floris/turbine_library/turbine_previewer.py
+++ b/floris/turbine_library/turbine_previewer.py
@@ -21,18 +21,11 @@
 import numpy as np
 from attrs import define, field
 
-from floris.simulation.turbine import (
-    Ct,
+from floris.simulation.turbine.turbine import (
     power,
+    thrust_coefficient,
     Turbine,
 )
-from floris.simulation.turbine_multi_dim import (
-    Ct_multidim,
-    multidim_Ct_down_select,
-    multidim_power_down_select,
-    power_multidim,
-    TurbineMultiDimensional,
-)
 from floris.type_dec import convert_to_path, NDArrayFloat
 from floris.utilities import (
     load_yaml,
@@ -47,9 +40,7 @@
 
 @define(auto_attribs=True)
 class TurbineInterface:
-    turbine: Turbine | TurbineMultiDimensional = field(
-        validator=attrs.validators.instance_of((Turbine, TurbineMultiDimensional))
-    )
+    turbine: Turbine = field(validator=attrs.validators.instance_of(Turbine))
 
     @classmethod
     def from_library(cls, library_path: str | Path, file_name: str):
@@ -72,9 +63,6 @@ def from_library(cls, library_path: str | Path, file_name: str):
 
         # Add in the library specification if needed, and load from dict
         turb_dict = load_yaml(library_path / file_name)
-        if turb_dict.get("multi_dimensional_cp_ct", False):
-            turb_dict.setdefault("turbine_library_path", library_path)
-            return cls(turbine=TurbineMultiDimensional.from_dict(turb_dict))
         return cls(turbine=Turbine.from_dict(turb_dict))
 
     @classmethod
@@ -92,9 +80,6 @@ def from_yaml(cls, file_path: str | Path):
 
         # Add in the library specification if needed, and load from dict
         turb_dict = load_yaml(file_path)
-        if turb_dict.get("multi_dimensional_cp_ct", False):
-            turb_dict.setdefault("turbine_library_path", file_path.parent)
-            return cls(turbine=TurbineMultiDimensional.from_dict(turb_dict))
         return cls(turbine=Turbine.from_dict(turb_dict))
 
     @classmethod
@@ -108,8 +93,6 @@ def from_turbine_dict(cls, config_dict: dict):
         Returns:
             (`TurbineInterface`): Returns a ``TurbineInterface`` object.
         """
-        if config_dict.get("multi_dimensional_cp_ct", False):
-            return cls(turbine=TurbineMultiDimensional.from_dict(config_dict))
         return cls(turbine=Turbine.from_dict(config_dict))
 
     def power_curve(
@@ -130,30 +113,35 @@ def power_curve(
         """
         shape = (wind_speeds.size, 1)
         if self.turbine.multi_dimensional_cp_ct:
-            power_interps = {
-                k: multidim_power_down_select(
-                    np.full(shape, self.turbine.power_interp),
-                    dict(zip(self.turbine.condition_keys, k)),
-                )
-                for k in self.turbine.power_interp
-            }
             power_mw = {
-                k: power_multidim(
-                    ref_air_density=np.full(shape, self.turbine.ref_air_density),
-                    rotor_effective_velocities=wind_speeds.reshape(shape),
-                    power_interp=power_interps[k],
+                k: power(
+                    velocities=wind_speeds.reshape(shape),
+                    air_density=np.full(shape, v["ref_air_density"]),
+                    power_functions={self.turbine.turbine_type: self.turbine.power_function},
+                    yaw_angles=np.zeros(shape),
+                    tilt_angles=np.full(shape, v["ref_tilt"]),
+                    tilt_interps={self.turbine.turbine_type: self.turbine.tilt_interp},
+                    turbine_type_map=np.full(shape, self.turbine.turbine_type),
+                    turbine_power_thrust_tables={self.turbine.turbine_type: v},
                 ).flatten() / 1e6
-                for k in self.turbine.power_interp
+                for k,v in self.turbine.power_thrust_table.items()
             }
         else:
             power_mw = power(
-                rotor_effective_velocities=wind_speeds.reshape(shape),
-                power_interp={self.turbine.turbine_type: self.turbine.power_interp},
-                turbine_type_map=np.full(shape, self.turbine.turbine_type)
+                velocities=wind_speeds.reshape(shape),
+                air_density=np.full(shape, self.turbine.power_thrust_table["ref_air_density"]),
+                power_functions={self.turbine.turbine_type: self.turbine.power_function},
+                yaw_angles=np.zeros(shape),
+                tilt_angles=np.full(shape, self.turbine.power_thrust_table["ref_tilt"]),
+                tilt_interps={self.turbine.turbine_type: self.turbine.tilt_interp},
+                turbine_type_map=np.full(shape, self.turbine.turbine_type),
+                turbine_power_thrust_tables={
+                    self.turbine.turbine_type: self.turbine.power_thrust_table
+                },
             ).flatten() / 1e6
         return wind_speeds, power_mw
 
-    def Ct_curve(
+    def thrust_coefficient_curve(
         self,
         wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS,
     ) -> tuple[NDArrayFloat, NDArrayFloat]:
@@ -169,38 +157,36 @@ def Ct_curve(
                 Returns the wind speed array and the thrust coefficient array.
         """
         shape = (wind_speeds.size, 1)
-        shape_single = (1, 1)
         if self.turbine.multi_dimensional_cp_ct:
-            fCt_interps = {
-                k: multidim_Ct_down_select(
-                    np.full(shape, self.turbine.fCt_interp),
-                    dict(zip(self.turbine.condition_keys, k)),
-                )
-                for k in self.turbine.fCt_interp
-            }
             ct_curve = {
-                k: Ct_multidim(
+                k: thrust_coefficient(
                     velocities=wind_speeds.reshape(shape),
-                    yaw_angle=np.zeros(shape),
-                    tilt_angle=np.full(shape, self.turbine.ref_tilt),
-                    ref_tilt=np.full(shape_single, self.turbine.ref_tilt),
-                    fCt=fCt_interps[k],
-                    tilt_interp={self.turbine.turbine_type: self.turbine.tilt_interp},
-                    correct_cp_ct_for_tilt=np.zeros(shape_single, dtype=bool),
-                    turbine_type_map=np.full(shape_single, self.turbine.turbine_type)
+                    yaw_angles=np.zeros(shape),
+                    tilt_angles=np.full(shape, v["ref_tilt"]),
+                    thrust_coefficient_functions={
+                        self.turbine.turbine_type: self.turbine.thrust_coefficient_function
+                    },
+                    tilt_interps={self.turbine.turbine_type: self.turbine.tilt_interp},
+                    correct_cp_ct_for_tilt=np.zeros(shape, dtype=bool),
+                    turbine_type_map=np.full(shape, self.turbine.turbine_type),
+                    turbine_power_thrust_tables={self.turbine.turbine_type: v},
                 ).flatten()
-                for k in self.turbine.fCt_interp
+                for k,v in self.turbine.power_thrust_table.items()
             }
         else:
-            ct_curve = Ct(
+            ct_curve = thrust_coefficient(
                 velocities=wind_speeds.reshape(shape),
-                yaw_angle=np.zeros(shape),
-                tilt_angle=np.full(shape, self.turbine.ref_tilt),
-                ref_tilt=np.full(shape, self.turbine.ref_tilt),
-                fCt={self.turbine.turbine_type: self.turbine.fCt_interp},
-                tilt_interp={self.turbine.turbine_type: self.turbine.tilt_interp},
+                yaw_angles=np.zeros(shape),
+                tilt_angles=np.full(shape, self.turbine.power_thrust_table["ref_tilt"]),
+                thrust_coefficient_functions={
+                    self.turbine.turbine_type: self.turbine.thrust_coefficient_function
+                },
+                tilt_interps={self.turbine.turbine_type: self.turbine.tilt_interp},
                 correct_cp_ct_for_tilt=np.zeros(shape, dtype=bool),
                 turbine_type_map=np.full(shape, self.turbine.turbine_type),
+                turbine_power_thrust_tables={
+                    self.turbine.turbine_type: self.turbine.power_thrust_table
+                },
             ).flatten()
         return wind_speeds, ct_curve
 
@@ -274,7 +260,7 @@ def plot_power_curve(
 
         fig.tight_layout()
 
-    def plot_Ct_curve(
+    def plot_thrust_coefficient_curve(
         self,
         wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS,
         fig_kwargs: dict | None =  None,
@@ -300,7 +286,7 @@ def plot_Ct_curve(
             None | tuple[plt.Figure, plt.Axes]: None, if :py:attr:`return_fig` is False, otherwise
                 a tuple of the Figure and Axes objects are returned.
         """
-        wind_speeds, thrust = self.Ct_curve(wind_speeds=wind_speeds)
+        wind_speeds, thrust = self.thrust_coefficient_curve(wind_speeds=wind_speeds)
 
         # Initialize kwargs if None
         fig_kwargs = {} if fig_kwargs is None else fig_kwargs
@@ -347,8 +333,7 @@ def plot_Ct_curve(
 class TurbineLibrary:
     turbine_map: dict[str: TurbineInterface] = field(factory=dict)
     power_curves: dict[str, tuple[NDArrayFloat, NDArrayFloat]] = field(factory=dict)
-    Cp_curves: dict[str, tuple[NDArrayFloat, NDArrayFloat]] = field(factory=dict)
-    Ct_curves: dict[str, tuple[NDArrayFloat, NDArrayFloat]] = field(factory=dict)
+    thrust_coefficient_curves: dict[str, tuple[NDArrayFloat, NDArrayFloat]] = field(factory=dict)
 
     def load_internal_library(self, which: list[str] = [], exclude: list[str] = []) -> None:
         """Loads all of the turbine configurations from ``floris/floris/turbine_libary``,
@@ -414,19 +399,19 @@ def compute_power_curves(
             name: t.power_curve(wind_speeds) for name, t in self.turbine_map.items()
         }
 
-    def compute_Ct_curves(
+    def compute_thrust_coefficient_curves(
             self,
             wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS,
         ) -> None:
         """Computes the thrust curves for each turbine in ``turbine_map`` and sets the
-        ``Ct_curves`` attribute.
+        ``thrust_coefficient_curves`` attribute.
 
         Args:
             wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to
                 0 m/s -> 40 m/s, every 0.5 m/s.
         """
-        self.Ct_curves = {
-            name: t.Ct_curve(wind_speeds) for name, t in self.turbine_map.items()
+        self.thrust_coefficient_curves = {
+            name: t.thrust_coefficient_curve(wind_speeds) for name, t in self.turbine_map.items()
         }
 
     def plot_power_curves(
@@ -522,7 +507,7 @@ def plot_power_curves(
         if show:
             fig.tight_layout()
 
-    def plot_Ct_curves(
+    def plot_thrust_coefficient_curves(
         self,
         fig: plt.Figure | None = None,
         ax: plt.Axes | None = None,
@@ -561,8 +546,8 @@ def plot_Ct_curves(
             None | tuple[plt.Figure, plt.Axes]: None, if :py:attr:`return_fig` is False, otherwise
                 a tuple of the Figure and Axes objects are returned.
         """
-        if self.Ct_curves == {} or wind_speeds is None:
-            self.compute_Ct_curves(wind_speeds=wind_speeds)
+        if self.thrust_coefficient_curves == {} or wind_speeds is None:
+            self.compute_thrust_coefficient_curves(wind_speeds=wind_speeds)
 
         which = [*self.turbine_map] if which == [] else which
 
@@ -584,7 +569,7 @@ def plot_Ct_curves(
         min_windspeed = 0
         max_windspeed = 0
         max_thrust = 0
-        for name, (ws, t) in self.Ct_curves.items():
+        for name, (ws, t) in self.thrust_coefficient_curves.items():
             if name in exclude or name not in which:
                 continue
             if isinstance(t, dict):
@@ -823,7 +808,7 @@ def plot_comparison(
             wind_speeds=wind_speeds,
             plot_kwargs=plot_kwargs,
         )
-        self.plot_Ct_curves(
+        self.plot_thrust_coefficient_curves(
             fig,
             ax3,
             which=which,
diff --git a/floris/tools/turbine_utilities.py b/floris/turbine_library/turbine_utilities.py
similarity index 97%
rename from floris/tools/turbine_utilities.py
rename to floris/turbine_library/turbine_utilities.py
index 65664b163..9de8dce6b 100644
--- a/floris/tools/turbine_utilities.py
+++ b/floris/turbine_library/turbine_utilities.py
@@ -12,13 +12,15 @@
 
 # See https://floris.readthedocs.io for documentation
 
-import os.path
+from __future__ import annotations
+
+from collections.abc import Iterable
 
 import numpy as np
 import yaml
 
 
-def build_turbine_dict(
+def build_cosine_loss_turbine_dict(
     turbine_data_dict,
     turbine_name,
     file_name=None,
@@ -142,6 +144,10 @@ def build_turbine_dict(
 
     # Build the turbine dict
     power_thrust_dict = {
+        "ref_air_density": ref_air_density,
+        "ref_tilt": ref_tilt,
+        "pP": pP,
+        "pT": pT,
         "wind_speed": u.tolist(),
         "power": p.tolist(),
         "thrust_coefficient": Ct.tolist()
@@ -151,12 +157,9 @@ def build_turbine_dict(
         "turbine_type": turbine_name,
         "generator_efficiency": generator_efficiency,
         "hub_height": hub_height,
-        "pP": pP,
-        "pT": pT,
         "rotor_diameter": rotor_diameter,
         "TSR": TSR,
-        "ref_air_density": ref_air_density,
-        "ref_tilt": ref_tilt,
+        "power_thrust_model": "cosine-loss",
         "power_thrust_table": power_thrust_dict
     }
 
diff --git a/setup.py b/setup.py
index a31e1e0f3..c1a06a593 100644
--- a/setup.py
+++ b/setup.py
@@ -42,7 +42,6 @@
 
     # utilities
     "coloredlogs~=10.0",
-    "flatten_dict~=0.0",
 ]
 
 # What packages are optional?
diff --git a/tests/conftest.py b/tests/conftest.py
index 5feafbee0..d1aefa535 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -203,12 +203,13 @@ def __init__(self):
             "turbine_type": "nrel_5mw",
             "rotor_diameter": 126.0,
             "hub_height": 90.0,
-            "pP": 1.88,
-            "pT": 1.88,
             "generator_efficiency": 1.0,
-            "ref_air_density": 1.225,
-            "ref_tilt": 5.0,
+            "power_thrust_model": "cosine-loss",
             "power_thrust_table": {
+                "pP": 1.88,
+                "pT": 1.88,
+                "ref_air_density": 1.225,
+                "ref_tilt": 5.0,
                 "power": [
                     0.0,
                     0.0,
@@ -379,9 +380,11 @@ def __init__(self):
         self.turbine_floating["correct_cp_ct_for_tilt"] = True
 
         self.turbine_multi_dim = copy.deepcopy(self.turbine)
-        del self.turbine_multi_dim['power_thrust_table']
+        del self.turbine_multi_dim['power_thrust_table']['power']
+        del self.turbine_multi_dim['power_thrust_table']['thrust_coefficient']
+        del self.turbine_multi_dim['power_thrust_table']['wind_speed']
         self.turbine_multi_dim["multi_dimensional_cp_ct"] = True
-        self.turbine_multi_dim["power_thrust_data_file"] = ""
+        self.turbine_multi_dim['power_thrust_table']["power_thrust_data_file"] = ""
 
         self.farm = {
             "layout_x": X_COORDS,
diff --git a/tests/reg_tests/cumulative_curl_regression_test.py b/tests/reg_tests/cumulative_curl_regression_test.py
index ffdc8bdd9..f5e58caa2 100644
--- a/tests/reg_tests/cumulative_curl_regression_test.py
+++ b/tests/reg_tests/cumulative_curl_regression_test.py
@@ -17,10 +17,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     Floris,
     power,
     rotor_effective_velocity,
+    thrust_coefficient,
 )
 from tests.conftest import (
     assert_results_arrays,
@@ -178,43 +178,35 @@ def test_regression_tandem(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -339,43 +331,35 @@ def test_regression_yaw(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -428,43 +412,35 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -516,43 +492,35 @@ def test_regression_secondary_steering(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -614,23 +582,15 @@ def test_regression_small_grid_rotation(sample_inputs_fixture):
     yaw_angles = floris.farm.yaw_angles
     tilt_angles = floris.farm.tilt_angles
 
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
 
     # A "column" is oriented parallel to the wind direction
diff --git a/tests/reg_tests/empirical_gauss_regression_test.py b/tests/reg_tests/empirical_gauss_regression_test.py
index 36bf4b248..6d798afa2 100644
--- a/tests/reg_tests/empirical_gauss_regression_test.py
+++ b/tests/reg_tests/empirical_gauss_regression_test.py
@@ -17,10 +17,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     Floris,
     power,
     rotor_effective_velocity,
+    thrust_coefficient,
 )
 from tests.conftest import (
     assert_results_arrays,
@@ -151,43 +151,35 @@ def test_regression_tandem(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
+        floris.farm.yaw_angles,
+        floris.farm.tilt_angles,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -315,43 +307,35 @@ def test_regression_yaw(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
+        floris.farm.yaw_angles,
+        floris.farm.tilt_angles,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -405,43 +389,35 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
+        floris.farm.yaw_angles,
+        floris.farm.tilt_angles,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -478,43 +454,35 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
+        floris.farm.yaw_angles,
+        floris.farm.tilt_angles,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -575,26 +543,29 @@ def test_regression_small_grid_rotation(sample_inputs_fixture):
 
     # farm_avg_velocities = average_velocity(floris.flow_field.u)
     velocities = floris.flow_field.u
-    yaw_angles = floris.farm.yaw_angles
-    tilt_angles = floris.farm.tilt_angles
 
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    # farm_eff_velocities = rotor_effective_velocity(
+    #     floris.flow_field.air_density,
+    #     floris.farm.ref_air_densities,
+    #     velocities,
+    #     yaw_angles,
+    #     tilt_angles,
+    #     floris.farm.ref_tilts,
+    #     floris.farm.pPs,
+    #     floris.farm.pTs,
+    #     floris.farm.turbine_tilt_interps,
+    #     floris.farm.correct_cp_ct_for_tilt,
+    #     floris.farm.turbine_type_map,
+    # )
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
+        floris.farm.yaw_angles,
+        floris.farm.tilt_angles,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
 
     # A "column" is oriented parallel to the wind direction
diff --git a/tests/reg_tests/floris_interface_regression_test.py b/tests/reg_tests/floris_interface_regression_test.py
index e9164f3a5..8cda5f9e3 100644
--- a/tests/reg_tests/floris_interface_regression_test.py
+++ b/tests/reg_tests/floris_interface_regression_test.py
@@ -17,10 +17,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     power,
+    thrust_coefficient,
 )
-from floris.simulation.turbine import rotor_effective_velocity
+from floris.simulation.rotor_velocity import rotor_effective_velocity
 from floris.tools import FlorisInterface
 from tests.conftest import (
     assert_results_arrays,
@@ -91,43 +91,35 @@ def test_calculate_no_wake(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        fi.floris.flow_field.air_density,
-        fi.floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        fi.floris.farm.ref_tilts,
-        fi.floris.farm.pPs,
-        fi.floris.farm.pTs,
+        fi.floris.farm.turbine_thrust_coefficient_functions,
         fi.floris.farm.turbine_tilt_interps,
         fi.floris.farm.correct_cp_ct_for_tilt,
         fi.floris.farm.turbine_type_map,
+        fi.floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        fi.floris.farm.ref_tilts,
-        fi.floris.farm.turbine_fCts,
+        fi.floris.flow_field.air_density,
+        fi.floris.farm.turbine_power_functions,
+        fi.floris.farm.yaw_angles,
+        fi.floris.farm.tilt_angles,
         fi.floris.farm.turbine_tilt_interps,
-        fi.floris.farm.correct_cp_ct_for_tilt,
-        fi.floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        fi.floris.farm.turbine_power_interps,
         fi.floris.farm.turbine_type_map,
+        fi.floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        fi.floris.farm.ref_tilts,
-        fi.floris.farm.turbine_fCts,
+        fi.floris.farm.turbine_axial_induction_functions,
         fi.floris.farm.turbine_tilt_interps,
         fi.floris.farm.correct_cp_ct_for_tilt,
         fi.floris.farm.turbine_type_map,
+        fi.floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
diff --git a/tests/reg_tests/gauss_regression_test.py b/tests/reg_tests/gauss_regression_test.py
index 084684c33..679023d54 100644
--- a/tests/reg_tests/gauss_regression_test.py
+++ b/tests/reg_tests/gauss_regression_test.py
@@ -17,10 +17,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     Floris,
     power,
     rotor_effective_velocity,
+    thrust_coefficient,
 )
 from tests.conftest import (
     assert_results_arrays,
@@ -269,43 +269,35 @@ def test_regression_tandem(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -430,43 +422,35 @@ def test_regression_yaw(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -516,43 +500,35 @@ def test_regression_gch(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -598,43 +574,35 @@ def test_regression_gch(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -687,43 +655,35 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -775,43 +735,35 @@ def test_regression_secondary_steering(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -873,23 +825,15 @@ def test_regression_small_grid_rotation(sample_inputs_fixture):
     yaw_angles = floris.farm.yaw_angles
     tilt_angles = floris.farm.tilt_angles
 
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
 
     # A "column" is oriented parallel to the wind direction
diff --git a/tests/reg_tests/jensen_jimenez_regression_test.py b/tests/reg_tests/jensen_jimenez_regression_test.py
index 8c97185c6..1122b42f2 100644
--- a/tests/reg_tests/jensen_jimenez_regression_test.py
+++ b/tests/reg_tests/jensen_jimenez_regression_test.py
@@ -17,10 +17,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     Floris,
     power,
     rotor_effective_velocity,
+    thrust_coefficient,
 )
 from tests.conftest import (
     assert_results_arrays,
@@ -120,43 +120,35 @@ def test_regression_tandem(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
-        yaw_angles,
-        tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
+        floris.farm.yaw_angles,
+        floris.farm.tilt_angles,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -281,43 +273,35 @@ def test_regression_yaw(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -379,23 +363,28 @@ def test_regression_small_grid_rotation(sample_inputs_fixture):
     yaw_angles = floris.farm.yaw_angles
     tilt_angles = floris.farm.tilt_angles
 
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    # farm_eff_velocities = rotor_effective_velocity(
+    #     floris.flow_field.air_density,
+    #     floris.farm.ref_air_densities,
+    #     velocities,
+    #     yaw_angles,
+    #     tilt_angles,
+    #     floris.farm.ref_tilts,
+    #     floris.farm.pPs,
+    #     floris.farm.pTs,
+    #     floris.farm.turbine_tilt_interps,
+    #     floris.farm.correct_cp_ct_for_tilt,
+    #     floris.farm.turbine_type_map,
+    # )
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
 
     # A "column" is oriented parallel to the wind direction
diff --git a/tests/reg_tests/none_regression_test.py b/tests/reg_tests/none_regression_test.py
index c7281c082..6b4c23235 100644
--- a/tests/reg_tests/none_regression_test.py
+++ b/tests/reg_tests/none_regression_test.py
@@ -18,10 +18,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     Floris,
     power,
     rotor_effective_velocity,
+    thrust_coefficient,
 )
 from tests.conftest import (
     assert_results_arrays,
@@ -121,43 +121,35 @@ def test_regression_tandem(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -315,23 +307,15 @@ def test_regression_small_grid_rotation(sample_inputs_fixture):
     yaw_angles = floris.farm.yaw_angles
     tilt_angles = floris.farm.tilt_angles
 
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
 
     # A "column" is oriented parallel to the wind direction
diff --git a/tests/reg_tests/turbopark_regression_test.py b/tests/reg_tests/turbopark_regression_test.py
index fd64c4c1b..144bdd6f2 100644
--- a/tests/reg_tests/turbopark_regression_test.py
+++ b/tests/reg_tests/turbopark_regression_test.py
@@ -17,10 +17,10 @@
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     Floris,
     power,
     rotor_effective_velocity,
+    thrust_coefficient,
 )
 from tests.conftest import (
     assert_results_arrays,
@@ -122,43 +122,35 @@ def test_regression_tandem(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -284,43 +276,35 @@ def test_regression_yaw(sample_inputs_fixture):
     farm_avg_velocities = average_velocity(
         velocities,
     )
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_cts = thrust_coefficient(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
+        floris.farm.turbine_thrust_coefficient_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
-    farm_cts = Ct(
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     farm_axial_inductions = axial_induction(
         velocities,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.turbine_fCts,
+        floris.farm.turbine_axial_induction_functions,
         floris.farm.turbine_tilt_interps,
         floris.farm.correct_cp_ct_for_tilt,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
     for i in range(n_findex):
         for j in range(n_turbines):
@@ -377,23 +361,15 @@ def test_regression_small_grid_rotation(sample_inputs_fixture):
     yaw_angles = floris.farm.yaw_angles
     tilt_angles = floris.farm.tilt_angles
 
-    farm_eff_velocities = rotor_effective_velocity(
-        floris.flow_field.air_density,
-        floris.farm.ref_air_densities,
+    farm_powers = power(
         velocities,
+        floris.flow_field.air_density,
+        floris.farm.turbine_power_functions,
         yaw_angles,
         tilt_angles,
-        floris.farm.ref_tilts,
-        floris.farm.pPs,
-        floris.farm.pTs,
         floris.farm.turbine_tilt_interps,
-        floris.farm.correct_cp_ct_for_tilt,
-        floris.farm.turbine_type_map,
-    )
-    farm_powers = power(
-        farm_eff_velocities,
-        floris.farm.turbine_power_interps,
         floris.farm.turbine_type_map,
+        floris.farm.turbine_power_thrust_tables,
     )
 
     # A "column" is oriented parallel to the wind direction
diff --git a/tests/rotor_velocity_unit_test.py b/tests/rotor_velocity_unit_test.py
new file mode 100644
index 000000000..c90892752
--- /dev/null
+++ b/tests/rotor_velocity_unit_test.py
@@ -0,0 +1,198 @@
+import numpy as np
+
+from floris.simulation import Turbine
+from floris.simulation.rotor_velocity import (
+    average_velocity,
+    compute_tilt_angles_for_floating_turbines,
+    compute_tilt_angles_for_floating_turbines_map,
+    cubic_cubature,
+    rotor_velocity_tilt_correction,
+    rotor_velocity_yaw_correction,
+    simple_cubature,
+)
+from tests.conftest import SampleInputs, WIND_SPEEDS
+
+
+def test_rotor_velocity_yaw_correction():
+    N_TURBINES = 4
+
+    wind_speed = average_velocity(10.0 * np.ones((1, 1, 3, 3)))
+    wind_speed_N_TURBINES = average_velocity(10.0 * np.ones((1, N_TURBINES, 3, 3)))
+
+    # Test a single turbine for zero yaw
+    yaw_corrected_velocities = rotor_velocity_yaw_correction(
+        pP=3.0,
+        yaw_angles=0.0,
+        rotor_effective_velocities=wind_speed,
+    )
+    np.testing.assert_allclose(yaw_corrected_velocities, wind_speed)
+
+    # Test a single turbine for non-zero yaw
+    yaw_corrected_velocities = rotor_velocity_yaw_correction(
+        pP=3.0,
+        yaw_angles=60.0,
+        rotor_effective_velocities=wind_speed,
+    )
+    np.testing.assert_allclose(yaw_corrected_velocities, 0.5 * wind_speed)
+
+    # Test multiple turbines for zero yaw
+    yaw_corrected_velocities = rotor_velocity_yaw_correction(
+        pP=3.0,
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+    np.testing.assert_allclose(yaw_corrected_velocities, wind_speed_N_TURBINES)
+
+    # Test multiple turbines for non-zero yaw
+    yaw_corrected_velocities = rotor_velocity_yaw_correction(
+        pP=3.0,
+        yaw_angles=np.ones((1, N_TURBINES)) * 60.0,
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+    np.testing.assert_allclose(yaw_corrected_velocities, 0.5 * wind_speed_N_TURBINES)
+
+
+def test_rotor_velocity_tilt_correction():
+    N_TURBINES = 4
+
+    wind_speed = average_velocity(10.0 * np.ones((1, 1, 3, 3)))
+    wind_speed_N_TURBINES = average_velocity(10.0 * np.ones((1, N_TURBINES, 3, 3)))
+
+    turbine_data = SampleInputs().turbine
+    turbine_floating_data = SampleInputs().turbine_floating
+    turbine = Turbine.from_dict(turbine_data)
+    turbine_floating = Turbine.from_dict(turbine_floating_data)
+    turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
+    turbine_type_map = turbine_type_map[None, :]
+
+    # Test single non-floating turbine
+    tilt_corrected_velocities = rotor_velocity_tilt_correction(
+        #turbine_type_map=np.array([turbine_type_map[:, 0]]),
+        tilt_angles=5.0*np.ones((1, 1)),
+        ref_tilt=np.array([turbine.power_thrust_table["ref_tilt"]]),
+        pT=np.array([turbine.power_thrust_table["pT"]]),
+        tilt_interp=turbine.tilt_interp,
+        correct_cp_ct_for_tilt=np.array([[False]]),
+        rotor_effective_velocities=wind_speed,
+    )
+
+    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed)
+
+    # Test multiple non-floating turbines
+    tilt_corrected_velocities = rotor_velocity_tilt_correction(
+        #turbine_type_map=turbine_type_map,
+        tilt_angles=5.0*np.ones((1, N_TURBINES)),
+        ref_tilt=np.array([turbine.power_thrust_table["ref_tilt"]] * N_TURBINES),
+        pT=np.array([turbine.power_thrust_table["pT"]] * N_TURBINES),
+        tilt_interp=turbine.tilt_interp,
+        correct_cp_ct_for_tilt=np.array([[False] * N_TURBINES]),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+
+    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed_N_TURBINES)
+
+    # Test single floating turbine
+    tilt_corrected_velocities = rotor_velocity_tilt_correction(
+        #turbine_type_map=np.array([turbine_type_map[:, 0]]),
+        tilt_angles=5.0*np.ones((1, 1)),
+        ref_tilt=np.array([turbine_floating.power_thrust_table["ref_tilt"]]),
+        pT=np.array([turbine_floating.power_thrust_table["pT"]]),
+        tilt_interp=turbine_floating.tilt_interp,
+        correct_cp_ct_for_tilt=np.array([[True]]),
+        rotor_effective_velocities=wind_speed,
+    )
+
+    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed)
+
+    # Test multiple floating turbines
+    tilt_corrected_velocities = rotor_velocity_tilt_correction(
+        #turbine_type_map,
+        tilt_angles=5.0*np.ones((1, N_TURBINES)),
+        ref_tilt=np.array([turbine_floating.power_thrust_table["ref_tilt"]] * N_TURBINES),
+        pT=np.array([turbine_floating.power_thrust_table["pT"]] * N_TURBINES),
+        tilt_interp=turbine_floating.tilt_interp,
+        correct_cp_ct_for_tilt=np.array([[True] * N_TURBINES]),
+        rotor_effective_velocities=wind_speed_N_TURBINES,
+    )
+
+    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed_N_TURBINES)
+
+def test_compute_tilt_angles_for_floating_turbines():
+    N_TURBINES = 4
+
+    wind_speed = 25.0
+    rotor_effective_velocities = average_velocity(wind_speed * np.ones((1, 1, 3, 3)))
+    rotor_effective_velocities_N_TURBINES = average_velocity(
+        wind_speed * np.ones((1, N_TURBINES, 3, 3))
+    )
+
+    turbine_floating_data = SampleInputs().turbine_floating
+    turbine_floating = Turbine.from_dict(turbine_floating_data)
+    turbine_type_map = np.array(N_TURBINES * [turbine_floating.turbine_type])
+    turbine_type_map = turbine_type_map[None, :]
+
+    # Single turbine
+    tilt = compute_tilt_angles_for_floating_turbines(
+        #turbine_type_map=np.array([turbine_type_map[:, 0]]),
+        tilt_angles=5.0*np.ones((1, 1)),
+        tilt_interp=turbine_floating.tilt_interp,
+        rotor_effective_velocities=rotor_effective_velocities,
+    )
+
+    # calculate tilt again
+    truth_index = turbine_floating_data["floating_tilt_table"]["wind_speed"].index(wind_speed)
+    tilt_truth = turbine_floating_data["floating_tilt_table"]["tilt"][truth_index]
+    np.testing.assert_allclose(tilt, tilt_truth)
+
+    # Multiple turbines
+    tilt_N_turbines = compute_tilt_angles_for_floating_turbines_map(
+        turbine_type_map=np.array(turbine_type_map),
+        tilt_angles=5.0*np.ones((1, N_TURBINES)),
+        tilt_interps={turbine_floating.turbine_type: turbine_floating.tilt_interp},
+        rotor_effective_velocities=rotor_effective_velocities_N_TURBINES,
+    )
+
+    # calculate tilt again
+    truth_index = turbine_floating_data["floating_tilt_table"]["wind_speed"].index(wind_speed)
+    tilt_truth = turbine_floating_data["floating_tilt_table"]["tilt"][truth_index]
+    np.testing.assert_allclose(tilt_N_turbines, [[tilt_truth] * N_TURBINES])
+
+def test_simple_cubature():
+
+    # Define a velocity array
+    velocities = np.ones((1, 1, 3, 3))
+
+    # Define sample cubature weights
+    cubature_weights = np.array([1., 1., 1.])
+
+    # Define the axis as last 2 dimensions
+    axis = (velocities.ndim-2, velocities.ndim-1)
+
+    # Calculate expected output based on the given inputs
+    expected_output = 1.0
+
+    # Call the function with the given inputs
+    result = simple_cubature(velocities, cubature_weights, axis)
+
+    # Check if the result matches the expected output
+    np.testing.assert_allclose(result, expected_output)
+
+def test_cubic_cubature():
+
+    # Define a velocity array
+    velocities = np.ones((1, 1, 3, 3))
+
+    # Define sample cubature weights
+    cubature_weights = np.array([1., 1., 1.])
+
+    # Define the axis as last 2 dimensions
+    axis = (velocities.ndim-2, velocities.ndim-1)
+
+    # Calculate expected output based on the given inputs
+    expected_output = 1.0
+
+    # Call the function with the given inputs
+    result = cubic_cubature(velocities, cubature_weights, axis)
+
+    # Check if the result matches the expected output
+    np.testing.assert_allclose(result, expected_output)
diff --git a/tests/turbine_multi_dim_unit_test.py b/tests/turbine_multi_dim_unit_test.py
index a4af63040..7cd7e176a 100644
--- a/tests/turbine_multi_dim_unit_test.py
+++ b/tests/turbine_multi_dim_unit_test.py
@@ -21,15 +21,11 @@
 
 from floris.simulation import (
     Turbine,
-    TurbineMultiDimensional,
 )
-from floris.simulation.turbine_multi_dim import (
-    axial_induction_multidim,
-    Ct_multidim,
-    multidim_Ct_down_select,
-    multidim_power_down_select,
-    MultiDimensionalPowerThrustTable,
-    power_multidim,
+from floris.simulation.turbine.turbine import (
+    axial_induction,
+    power,
+    thrust_coefficient,
 )
 from tests.conftest import SampleInputs, WIND_SPEEDS
 
@@ -44,65 +40,44 @@
 
 INDEX_FILTER = [0, 2]
 
-
-def test_multidim_Ct_down_select():
-    CONDITIONS = {'Tp': 2, 'Hs': 1}
-
-    turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    turbine = TurbineMultiDimensional.from_dict(turbine_data)
-
-    downselect_turbine_fCts = multidim_Ct_down_select([[turbine.fCt_interp]], CONDITIONS)
-
-    assert downselect_turbine_fCts == turbine.fCt_interp[(2, 1)]
-
-
-def test_multidim_power_down_select():
-    CONDITIONS = {'Tp': 2, 'Hs': 1}
-
-    turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    turbine = TurbineMultiDimensional.from_dict(turbine_data)
-
-    downselect_power_interps = multidim_power_down_select([[turbine.power_interp]], CONDITIONS)
-
-    assert downselect_power_interps == turbine.power_interp[(2, 1)]
-
-
-def test_multi_dimensional_power_thrust_table():
-    turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    df_data = pd.read_csv(turbine_data["power_thrust_data_file"])
-    flattened_dict = MultiDimensionalPowerThrustTable.from_dataframe(df_data)
-    flattened_dict_base = {
-        ('Tp', '2', 'Hs', '1'): [],
-        ('Tp', '2', 'Hs', '5'): [],
-        ('Tp', '4', 'Hs', '1'): [],
-        ('Tp', '4', 'Hs', '5'): [],
-    }
-    assert flattened_dict == flattened_dict_base
-
-    # Test for initialization errors
-    for el in ("ws", "Cp", "Ct"):
-        df_data = pd.read_csv(turbine_data["power_thrust_data_file"])
-        df = df_data.drop(el, axis=1)
-        with pytest.raises(ValueError):
-            MultiDimensionalPowerThrustTable.from_dataframe(df)
+# NOTE: MultiDimensionalPowerThrustTable not used anywhere, so I'm commenting
+# this out.
+
+# def test_multi_dimensional_power_thrust_table():
+#     turbine_data = SampleInputs().turbine_multi_dim
+#     turbine_data["power_thrust_data_file"] = CSV_INPUT
+#     df_data = pd.read_csv(turbine_data["power_thrust_data_file"])
+#     flattened_dict = MultiDimensionalPowerThrustTable.from_dataframe(df_data)
+#     flattened_dict_base = {
+#         ('Tp', '2', 'Hs', '1'): [],
+#         ('Tp', '2', 'Hs', '5'): [],
+#         ('Tp', '4', 'Hs', '1'): [],
+#         ('Tp', '4', 'Hs', '5'): [],
+#     }
+#     assert flattened_dict == flattened_dict_base
+
+#     # Test for initialization errors
+#     for el in ("ws", "Cp", "Ct"):
+#         df_data = pd.read_csv(turbine_data["power_thrust_data_file"])
+#         df = df_data.drop(el, axis=1)
+#         with pytest.raises(ValueError):
+#             MultiDimensionalPowerThrustTable.from_dataframe(df)
 
 
 def test_turbine_init():
     turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    turbine = TurbineMultiDimensional.from_dict(turbine_data)
+    turbine_data["power_thrust_table"]["power_thrust_data_file"] = CSV_INPUT
+    turbine = Turbine.from_dict(turbine_data)
+    condition = (2, 1)
     assert turbine.rotor_diameter == turbine_data["rotor_diameter"]
     assert turbine.hub_height == turbine_data["hub_height"]
-    assert turbine.pP == turbine_data["pP"]
-    assert turbine.pT == turbine_data["pT"]
+    assert turbine.power_thrust_table[condition]["pP"] == turbine_data["power_thrust_table"]["pP"]
+    assert turbine.power_thrust_table[condition]["pT"] == turbine_data["power_thrust_table"]["pT"]
     assert turbine.generator_efficiency == turbine_data["generator_efficiency"]
 
-    assert isinstance(turbine.power_thrust_data, dict)
-    assert isinstance(turbine.fCt_interp, dict)
-    assert isinstance(turbine.power_interp, dict)
+    assert isinstance(turbine.power_thrust_table, dict)
+    assert callable(turbine.thrust_coefficient_function)
+    assert callable(turbine.power_function)
     assert turbine.rotor_radius == turbine_data["rotor_diameter"] / 2.0
 
 
@@ -110,45 +85,42 @@ def test_ct():
     N_TURBINES = 4
 
     turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    turbine = TurbineMultiDimensional.from_dict(turbine_data)
+    turbine_data["power_thrust_table"]["power_thrust_data_file"] = CSV_INPUT
+    turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
+    condition = (2, 1)
 
     # Single turbine
     # yaw angle / fCt are (n wind direction, n wind speed, n turbine)
     wind_speed = 10.0
-    thrust = Ct_multidim(
+    thrust = thrust_coefficient(
         velocities=wind_speed * np.ones((1, 1, 3, 3)),
-        yaw_angle=np.zeros((1, 1)),
-        tilt_angle=np.ones((1, 1)) * 5.0,
-        ref_tilt=np.ones((1, 1)) * 5.0,
-        fCt=np.array([[turbine.fCt_interp[(2, 1)]]]),
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, 1)),
+        tilt_angles=np.ones((1, 1)) * 5.0,
+        thrust_coefficient_functions={turbine.turbine_type: turbine.thrust_coefficient_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False]]),
-        turbine_type_map=turbine_type_map[:,0]
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
+        multidim_condition=condition
     )
 
     np.testing.assert_allclose(thrust, np.array([[0.77853469]]))
 
     # Multiple turbines with index filter
     # 4 turbines with 3 x 3 grid arrays
-    thrusts = Ct_multidim(
+    thrusts = thrust_coefficient(
         velocities=np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST,  # 16 x 4 x 3 x 3
-        yaw_angle=np.zeros((1, N_TURBINES)),
-        tilt_angle=np.ones((1, N_TURBINES)) * 5.0,
-        ref_tilt=np.ones((1, N_TURBINES)) * 5.0,
-        fCt=np.tile(
-            [turbine.fCt_interp[(2, 1)]],
-            (
-                np.shape(WIND_CONDITION_BROADCAST)[0],
-                N_TURBINES,
-            )
-        ),
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        tilt_angles=np.ones((1, N_TURBINES)) * 5.0,
+        thrust_coefficient_functions={turbine.turbine_type: turbine.thrust_coefficient_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False] * N_TURBINES]),
         turbine_type_map=turbine_type_map,
         ix_filter=INDEX_FILTER,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
+        multidim_condition=condition
     )
     assert len(thrusts[0]) == len(INDEX_FILTER)
 
@@ -175,54 +147,59 @@ def test_ct():
     ])
     np.testing.assert_allclose(thrusts, thrusts_truth)
 
-
 def test_power():
     N_TURBINES = 4
     AIR_DENSITY = 1.225
 
     turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    turbine = TurbineMultiDimensional.from_dict(turbine_data)
+    turbine_data["power_thrust_table"]["power_thrust_data_file"] = CSV_INPUT
+    turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
+    condition = (2, 1)
 
     # Single turbine
     wind_speed = 10.0
-    p = power_multidim(
-        ref_air_density=AIR_DENSITY,
-        rotor_effective_velocities=wind_speed * np.ones((1, 1, 3, 3)),
-        power_interp=np.array([[turbine.power_interp[(2, 1)]]]),
+    p = power(
+        velocities=wind_speed * np.ones((1, 1, 3, 3)),
+        air_density=AIR_DENSITY,
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, 1)), # 1 findex, 1 turbine
+        tilt_angles=turbine.power_thrust_table[condition]["ref_tilt"] * np.ones((1, 1)),
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
+        multidim_condition=condition
     )
 
-    power_truth = [
-        [
-            [
-                [3215682.686486, 3215682.686486, 3215682.686486],
-                [3215682.686486, 3215682.686486, 3215682.686486],
-                [3215682.686486, 3215682.686486, 3215682.686486],
-            ]
-        ]
-    ]
+    power_truth = 3215682.686486
 
-    np.testing.assert_allclose(p, power_truth )
+    np.testing.assert_allclose(p, power_truth)
 
     # Multiple turbines with ix filter
-    rotor_effective_velocities = np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST
-    p = power_multidim(
-        ref_air_density=AIR_DENSITY,
-        rotor_effective_velocities=rotor_effective_velocities,
-        power_interp=np.tile(
-            [turbine.power_interp[(2, 1)]],
-            (
-                np.shape(WIND_CONDITION_BROADCAST)[0],
-                N_TURBINES,
-            )
-        ),
+    velocities = np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST
+    p = power(
+        velocities=np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST,  # 16 x 4 x 3 x 3
+        air_density=AIR_DENSITY,
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        tilt_angles=np.ones((1, N_TURBINES)) * 5.0,
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map,
         ix_filter=INDEX_FILTER,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
+        multidim_condition=condition
     )
     assert len(p[0]) == len(INDEX_FILTER)
 
-    power_truth = turbine.power_interp[(2, 1)](rotor_effective_velocities) * AIR_DENSITY
+    power_truth = turbine.power_function(
+        power_thrust_table=turbine.power_thrust_table[condition],
+        velocities=velocities,
+        air_density=AIR_DENSITY,
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        tilt_angles=np.ones((1, N_TURBINES)) * 5.0,
+        tilt_interp=turbine.tilt_interp,
+    )
     np.testing.assert_allclose(p, power_truth[:, INDEX_FILTER[0]:INDEX_FILTER[1]])
 
 
@@ -231,44 +208,41 @@ def test_axial_induction():
     N_TURBINES = 4
 
     turbine_data = SampleInputs().turbine_multi_dim
-    turbine_data["power_thrust_data_file"] = CSV_INPUT
-    turbine = TurbineMultiDimensional.from_dict(turbine_data)
+    turbine_data["power_thrust_table"]["power_thrust_data_file"] = CSV_INPUT
+    turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
+    condition = (2, 1)
 
     baseline_ai = 0.2646995
 
     # Single turbine
     wind_speed = 10.0
-    ai = axial_induction_multidim(
+    ai = axial_induction(
         velocities=wind_speed * np.ones((1, 1, 3, 3)),
-        yaw_angle=np.zeros((1, 1)),
-        tilt_angle=np.ones((1, 1)) * 5.0,
-        ref_tilt=np.ones((1, 1)) * 5.0,
-        fCt=np.array([[turbine.fCt_interp[(2, 1)]]]),
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, 1)),
+        tilt_angles=np.ones((1, 1)) * 5.0,
+        axial_induction_functions={turbine.turbine_type: turbine.axial_induction_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False]]),
         turbine_type_map=turbine_type_map[0,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
+        multidim_condition=condition
     )
     np.testing.assert_allclose(ai, baseline_ai)
 
     # Multiple turbines with ix filter
-    ai = axial_induction_multidim(
+    ai = axial_induction(
         velocities=np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST,  # 16 x 4 x 3 x 3
-        yaw_angle=np.zeros((1, N_TURBINES)),
-        tilt_angle=np.ones((1, N_TURBINES)) * 5.0,
-        ref_tilt=np.ones((1, N_TURBINES)) * 5.0,
-        fCt=np.tile(
-            [turbine.fCt_interp[(2, 1)]],
-            (
-                np.shape(WIND_CONDITION_BROADCAST)[0],
-                N_TURBINES,
-            )
-        ),
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        tilt_angles=np.ones((1, N_TURBINES)) * 5.0,
+        axial_induction_functions={turbine.turbine_type: turbine.axial_induction_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False] * N_TURBINES]),
         turbine_type_map=turbine_type_map,
         ix_filter=INDEX_FILTER,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
+        multidim_condition=condition
     )
 
     assert len(ai[0]) == len(INDEX_FILTER)
diff --git a/tests/turbine_operation_models_test.py b/tests/turbine_operation_models_test.py
new file mode 100644
index 000000000..517bb0be7
--- /dev/null
+++ b/tests/turbine_operation_models_test.py
@@ -0,0 +1,215 @@
+import numpy as np
+
+from floris.simulation.turbine.operation_models import (
+    CosineLossTurbine,
+    rotor_velocity_air_density_correction,
+    SimpleTurbine,
+)
+from floris.utilities import cosd
+from tests.conftest import SampleInputs, WIND_SPEEDS
+
+
+def test_rotor_velocity_air_density_correction():
+
+    wind_speed = 10.
+    ref_air_density = 1.225
+    test_density = 1.2
+
+    test_speed = rotor_velocity_air_density_correction(wind_speed, ref_air_density, ref_air_density)
+    assert test_speed == wind_speed
+
+    test_speed = rotor_velocity_air_density_correction(wind_speed, test_density, test_density)
+    assert test_speed == wind_speed
+
+    test_speed = rotor_velocity_air_density_correction(0., test_density, ref_air_density)
+    assert test_speed == 0.
+
+    test_speed = rotor_velocity_air_density_correction(wind_speed, test_density, ref_air_density)
+    assert np.allclose((test_speed/wind_speed)**3, test_density/ref_air_density)
+
+def test_submodel_attributes():
+
+    assert hasattr(SimpleTurbine, "power")
+    assert hasattr(SimpleTurbine, "thrust_coefficient")
+
+    assert hasattr(CosineLossTurbine, "power")
+    assert hasattr(CosineLossTurbine, "thrust_coefficient")
+
+def test_SimpleTurbine():
+
+    n_turbines = 1
+    wind_speed = 10.0
+    turbine_data = SampleInputs().turbine
+
+    # Check that power works as expected
+    test_power = SimpleTurbine.power(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=turbine_data["power_thrust_table"]["ref_air_density"], # Matches ref_air_density
+    )
+    truth_index = turbine_data["power_thrust_table"]["wind_speed"].index(wind_speed)
+    baseline_power = turbine_data["power_thrust_table"]["power"][truth_index] * 1000
+    assert np.allclose(baseline_power, test_power)
+
+    # Check that yaw and tilt angle have no effect
+    test_power = SimpleTurbine.power(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=turbine_data["power_thrust_table"]["ref_air_density"], # Matches ref_air_density
+        yaw_angles=20 * np.ones((1, n_turbines)),
+        tilt_angles=5 * np.ones((1, n_turbines))
+    )
+    assert np.allclose(baseline_power, test_power)
+
+    # Check that a lower air density decreases power appropriately
+    test_power = SimpleTurbine.power(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1,
+    )
+    assert test_power < baseline_power
+
+
+    # Check that thrust coefficient works as expected
+    test_Ct = SimpleTurbine.thrust_coefficient(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+    )
+    baseline_Ct = turbine_data["power_thrust_table"]["thrust_coefficient"][truth_index]
+    assert np.allclose(baseline_Ct, test_Ct)
+
+    # Check that yaw and tilt angle have no effect
+    test_Ct = SimpleTurbine.thrust_coefficient(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+        yaw_angles=20 * np.ones((1, n_turbines)),
+        tilt_angles=5 * np.ones((1, n_turbines))
+    )
+    assert np.allclose(baseline_Ct, test_Ct)
+
+
+    # Check that axial induction works as expected
+    test_ai = SimpleTurbine.axial_induction(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+    )
+    baseline_ai = (
+        1 - np.sqrt(1 - turbine_data["power_thrust_table"]["thrust_coefficient"][truth_index])
+    )/2
+    assert np.allclose(baseline_ai, test_ai)
+
+    # Check that yaw and tilt angle have no effect
+    test_ai = SimpleTurbine.axial_induction(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+        yaw_angles=20 * np.ones((1, n_turbines)),
+        tilt_angles=5 * np.ones((1, n_turbines))
+    )
+    assert np.allclose(baseline_ai, test_ai)
+
+def test_CosineLossTurbine():
+
+    n_turbines = 1
+    wind_speed = 10.0
+    turbine_data = SampleInputs().turbine
+
+    yaw_angles_nom = 0 * np.ones((1, n_turbines))
+    tilt_angles_nom = turbine_data["power_thrust_table"]["ref_tilt"] * np.ones((1, n_turbines))
+    yaw_angles_test = 20 * np.ones((1, n_turbines))
+    tilt_angles_test = 0 * np.ones((1, n_turbines))
+
+
+    # Check that power works as expected
+    test_power = CosineLossTurbine.power(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=turbine_data["power_thrust_table"]["ref_air_density"], # Matches ref_air_density
+        yaw_angles=yaw_angles_nom,
+        tilt_angles=tilt_angles_nom,
+        tilt_interp=None
+    )
+    truth_index = turbine_data["power_thrust_table"]["wind_speed"].index(wind_speed)
+    baseline_power = turbine_data["power_thrust_table"]["power"][truth_index] * 1000
+    assert np.allclose(baseline_power, test_power)
+
+    # Check that yaw and tilt angle have an effect
+    test_power = CosineLossTurbine.power(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=turbine_data["power_thrust_table"]["ref_air_density"], # Matches ref_air_density
+        yaw_angles=yaw_angles_test,
+        tilt_angles=tilt_angles_test,
+        tilt_interp=None
+    )
+    assert test_power < baseline_power
+
+    # Check that a lower air density decreases power appropriately
+    test_power = CosineLossTurbine.power(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1,
+        yaw_angles=yaw_angles_nom,
+        tilt_angles=tilt_angles_nom,
+        tilt_interp=None
+    )
+    assert test_power < baseline_power
+
+
+    # Check that thrust coefficient works as expected
+    test_Ct = CosineLossTurbine.thrust_coefficient(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+        yaw_angles=yaw_angles_nom,
+        tilt_angles=tilt_angles_nom,
+        tilt_interp=None
+    )
+    baseline_Ct = turbine_data["power_thrust_table"]["thrust_coefficient"][truth_index]
+    assert np.allclose(baseline_Ct, test_Ct)
+
+    # Check that yaw and tilt angle have the expected effect
+    test_Ct = CosineLossTurbine.thrust_coefficient(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+        yaw_angles=yaw_angles_test,
+        tilt_angles=tilt_angles_test,
+        tilt_interp=None
+    )
+    absolute_tilt = tilt_angles_test - turbine_data["power_thrust_table"]["ref_tilt"]
+    assert test_Ct == baseline_Ct * cosd(yaw_angles_test) * cosd(absolute_tilt)
+
+
+    # Check that thrust coefficient works as expected
+    test_ai = CosineLossTurbine.axial_induction(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+        yaw_angles=yaw_angles_nom,
+        tilt_angles=tilt_angles_nom,
+        tilt_interp=None
+    )
+    baseline_misalignment_loss = (
+        cosd(yaw_angles_nom)
+        * cosd(tilt_angles_nom - turbine_data["power_thrust_table"]["ref_tilt"])
+    )
+    baseline_ai = (
+        1 - np.sqrt(1 - turbine_data["power_thrust_table"]["thrust_coefficient"][truth_index])
+    ) / 2 / baseline_misalignment_loss
+    assert np.allclose(baseline_ai, test_ai)
+
+    # Check that yaw and tilt angle have the expected effect
+    test_ai = CosineLossTurbine.axial_induction(
+        power_thrust_table=turbine_data["power_thrust_table"],
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=1.1, # Unused
+        yaw_angles=yaw_angles_test,
+        tilt_angles=tilt_angles_test,
+        tilt_interp=None
+    )
+    absolute_tilt = tilt_angles_test - turbine_data["power_thrust_table"]["ref_tilt"]
+    assert test_Ct == baseline_Ct * cosd(yaw_angles_test) * cosd(absolute_tilt)
diff --git a/tests/turbine_unit_test.py b/tests/turbine_unit_test.py
index 67d92c90a..b23e10050 100644
--- a/tests/turbine_unit_test.py
+++ b/tests/turbine_unit_test.py
@@ -20,23 +20,14 @@
 import numpy as np
 import pytest
 import yaml
-from scipy.interpolate import interp1d
 
 from floris.simulation import (
     average_velocity,
     axial_induction,
-    Ct,
     power,
+    thrust_coefficient,
     Turbine,
 )
-from floris.simulation.turbine import (
-    _rotor_velocity_tilt_correction,
-    _rotor_velocity_yaw_correction,
-    compute_tilt_angles_for_floating_turbines,
-    cubic_cubature,
-    simple_cubature,
-)
-from floris.tools import build_turbine_dict
 from tests.conftest import SampleInputs, WIND_SPEEDS
 
 
@@ -53,12 +44,15 @@ def test_turbine_init():
     assert turbine.turbine_type == turbine_data["turbine_type"]
     assert turbine.rotor_diameter == turbine_data["rotor_diameter"]
     assert turbine.hub_height == turbine_data["hub_height"]
-    assert turbine.pP == turbine_data["pP"]
-    assert turbine.pT == turbine_data["pT"]
+    assert turbine.power_thrust_table["pP"] == turbine_data["power_thrust_table"]["pP"]
+    assert turbine.power_thrust_table["pT"] == turbine_data["power_thrust_table"]["pT"]
     assert turbine.TSR == turbine_data["TSR"]
     assert turbine.generator_efficiency == turbine_data["generator_efficiency"]
-    assert turbine.ref_air_density == turbine_data["ref_air_density"]
-    assert turbine.ref_tilt == turbine_data["ref_tilt"]
+    assert (
+        turbine.power_thrust_table["ref_air_density"]
+        == turbine_data["power_thrust_table"]["ref_air_density"]
+    )
+    assert turbine.power_thrust_table["ref_tilt"] == turbine_data["power_thrust_table"]["ref_tilt"]
     assert np.array_equal(
         turbine.power_thrust_table["wind_speed"],
         turbine_data["power_thrust_table"]["wind_speed"]
@@ -77,11 +71,11 @@ def test_turbine_init():
     # TODO: test these explicitly.
     # Test create a simpler interpolator and test that you get the values you expect
     # fCt_interp: interp1d = field(init=False)
-    # power_interp: interp1d = field(init=False)
+    # power_function: interp1d = field(init=False)
     # tilt_interp: interp1d = field(init=False, default=None)
 
-    assert isinstance(turbine.fCt_interp, interp1d)
-    assert isinstance(turbine.power_interp, interp1d)
+    assert callable(turbine.thrust_coefficient_function)
+    assert callable(turbine.power_function)
 
 
 def test_rotor_radius():
@@ -191,15 +185,15 @@ def test_ct():
     # Single turbine
     # yaw angle / fCt are (n_findex, n turbine)
     wind_speed = 10.0
-    thrust = Ct(
+    thrust = thrust_coefficient(
         velocities=wind_speed * np.ones((1, 1, 3, 3)),
-        yaw_angle=np.zeros((1, 1)),
-        tilt_angle=np.ones((1, 1)) * 5.0,
-        ref_tilt=np.ones((1, 1)) * 5.0,
-        fCt={turbine.turbine_type: turbine.fCt_interp},
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, 1)),
+        tilt_angles=np.ones((1, 1)) * 5.0,
+        thrust_coefficient_functions={turbine.turbine_type: turbine.thrust_coefficient_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False]]),
-        turbine_type_map=turbine_type_map[:,0]
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
 
     truth_index = turbine_data["power_thrust_table"]["wind_speed"].index(wind_speed)
@@ -210,15 +204,15 @@ def test_ct():
 
     # Multiple turbines with index filter
     # 4 turbines with 3 x 3 grid arrays
-    thrusts = Ct(
+    thrusts = thrust_coefficient(
         velocities=np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST,  # 12 x 4 x 3 x 3
-        yaw_angle=np.zeros((1, N_TURBINES)),
-        tilt_angle=np.ones((1, N_TURBINES)) * 5.0,
-        ref_tilt=np.ones((1, N_TURBINES)) * 5.0,
-        fCt={turbine.turbine_type: turbine.fCt_interp},
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        tilt_angles=np.ones((1, N_TURBINES)) * 5.0,
+        thrust_coefficient_functions={turbine.turbine_type: turbine.thrust_coefficient_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False] * N_TURBINES]),
         turbine_type_map=turbine_type_map,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
         ix_filter=INDEX_FILTER,
     )
     assert len(thrusts[0]) == len(INDEX_FILTER)
@@ -231,15 +225,17 @@ def test_ct():
         )
 
     # Single floating turbine; note that 'tilt_interp' is not set to None
-    thrust = Ct(
+    thrust = thrust_coefficient(
         velocities=wind_speed * np.ones((1, 1, 3, 3)), # One findex, one turbine
-        yaw_angle=np.zeros((1, 1)),
-        tilt_angle=np.ones((1, 1)) * 5.0,
-        ref_tilt=np.ones((1, 1)) * 5.0,
-        fCt={turbine.turbine_type: turbine_floating.fCt_interp},
-        tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp},
+        yaw_angles=np.zeros((1, 1)),
+        tilt_angles=np.ones((1, 1)) * 5.0,
+        thrust_coefficient_functions={
+            turbine.turbine_type: turbine_floating.thrust_coefficient_function
+        },
+        tilt_interps={turbine_floating.turbine_type: turbine_floating.tilt_interp},
         correct_cp_ct_for_tilt=np.array([[True]]),
-        turbine_type_map=turbine_type_map[:,0]
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
 
     truth_index = turbine_floating_data["power_thrust_table"]["wind_speed"].index(wind_speed)
@@ -260,9 +256,14 @@ def test_power():
     turbine_type_map = np.array(n_turbines * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
     test_power = power(
-        rotor_effective_velocities=wind_speed * np.ones((1, 1)), # 1 findex, 1 turbine
-        power_interp={turbine.turbine_type: turbine.power_interp},
-        turbine_type_map=turbine_type_map[:,0]
+        velocities=wind_speed * np.ones((1, 1, 3, 3)), # 1 findex, 1 turbine, 3x3 grid
+        air_density=turbine.power_thrust_table["ref_air_density"],
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, 1)), # 1 findex, 1 turbine
+        tilt_angles=turbine.power_thrust_table["ref_tilt"] * np.ones((1, 1)),
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
 
     # Recompute using the provided power
@@ -274,9 +275,14 @@ def test_power():
     # At rated, the power calculated should be 5MW since the test data is the NREL 5MW turbine
     wind_speed = 18.0
     rated_power = power(
-        rotor_effective_velocities=wind_speed * np.ones((1, 1, 1)),
-        power_interp={turbine.turbine_type: turbine.power_interp},
-        turbine_type_map=turbine_type_map[:,0]
+        velocities=wind_speed * np.ones((1, 1, 3, 3)),
+        air_density=turbine.power_thrust_table["ref_air_density"],
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, 1)), # 1 findex, 1 turbine
+        tilt_angles=turbine.power_thrust_table["ref_tilt"] * np.ones((1, 1)),
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
     assert np.allclose(rated_power, 5e6)
 
@@ -284,9 +290,14 @@ def test_power():
     # At wind speed = 0.0, the power should be 0 based on the provided Cp curve
     wind_speed = 0.0
     zero_power = power(
-        rotor_effective_velocities=wind_speed * np.ones((1, 1, 1)),
-        power_interp={turbine.turbine_type: turbine.power_interp},
-        turbine_type_map=turbine_type_map[:,0]
+        velocities=wind_speed * np.ones((1, 1, 3, 3)),
+        air_density=turbine.power_thrust_table["ref_air_density"],
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, 1)), # 1 findex, 1 turbine
+        tilt_angles=turbine.power_thrust_table["ref_tilt"] * np.ones((1, 1)),
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map[:,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
     assert np.allclose(zero_power, 0.0)
 
@@ -299,26 +310,36 @@ def test_power():
     turbine_type_map = np.array(n_turbines * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
     test_4_power = power(
-        rotor_effective_velocities=wind_speed * np.ones((1, 1, n_turbines)),
-        power_interp={turbine.turbine_type: turbine.power_interp},
-        turbine_type_map=turbine_type_map
+        velocities=wind_speed * np.ones((1, n_turbines, 3, 3)),
+        air_density=turbine.power_thrust_table["ref_air_density"],
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, n_turbines)),
+        tilt_angles=turbine.power_thrust_table["ref_tilt"] * np.ones((1, n_turbines)),
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
-    baseline_4_power = baseline_power * np.ones((1, 1, n_turbines))
+    baseline_4_power = baseline_power * np.ones((1, n_turbines))
     assert np.allclose(baseline_4_power, test_4_power)
     assert np.shape(baseline_4_power) == np.shape(test_4_power)
 
 
-    # Same as above but with the grid expanded in the velocities array
+    # Same as above but with the grid collapsed in the velocities array
     turbine_data = SampleInputs().turbine
     turbine = Turbine.from_dict(turbine_data)
     turbine_type_map = np.array(n_turbines * [turbine.turbine_type])
     turbine_type_map = turbine_type_map[None, :]
     test_grid_power = power(
-        rotor_effective_velocities=wind_speed * np.ones((1, 1, n_turbines, 3, 3)),
-        power_interp={turbine.turbine_type: turbine.power_interp},
-        turbine_type_map=turbine_type_map[:,0]
+        velocities=wind_speed * np.ones((1, n_turbines, 1)),
+        air_density=turbine.power_thrust_table["ref_air_density"],
+        power_functions={turbine.turbine_type: turbine.power_function},
+        yaw_angles=np.zeros((1, n_turbines)),
+        tilt_angles=turbine.power_thrust_table["ref_tilt"] * np.ones((1, n_turbines)),
+        tilt_interps={turbine.turbine_type: turbine.tilt_interp},
+        turbine_type_map=turbine_type_map,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
-    baseline_grid_power = baseline_power * np.ones((1, 1, n_turbines, 3, 3))
+    baseline_grid_power = baseline_power * np.ones((1, n_turbines))
     assert np.allclose(baseline_grid_power, test_grid_power)
     assert np.shape(baseline_grid_power) == np.shape(test_grid_power)
 
@@ -340,26 +361,26 @@ def test_axial_induction():
     wind_speed = 10.0
     ai = axial_induction(
         velocities=wind_speed * np.ones((1, 1, 3, 3)), # 1 findex, 1 Turbine
-        yaw_angle=np.zeros((1, 1)),
-        tilt_angle=np.ones((1, 1)) * 5.0,
-        ref_tilt=np.ones((1, 1)) * 5.0,
-        fCt={turbine.turbine_type: turbine.fCt_interp},
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, 1)),
+        tilt_angles=np.ones((1, 1)) * 5.0,
+        axial_induction_functions={turbine.turbine_type: turbine.axial_induction_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False]]),
         turbine_type_map=turbine_type_map[0,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
     np.testing.assert_allclose(ai, baseline_ai)
 
     # Multiple turbines with ix filter
     ai = axial_induction(
         velocities=np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST,  # 12 x 4 x 3 x 3
-        yaw_angle=np.zeros((1, N_TURBINES)),
-        tilt_angle=np.ones((1, N_TURBINES)) * 5.0,
-        ref_tilt=np.ones((1, N_TURBINES)) * 5.0,
-        fCt={turbine.turbine_type: turbine.fCt_interp},
-        tilt_interp={turbine.turbine_type: None},
+        yaw_angles=np.zeros((1, N_TURBINES)),
+        tilt_angles=np.ones((1, N_TURBINES)) * 5.0,
+        axial_induction_functions={turbine.turbine_type: turbine.axial_induction_function},
+        tilt_interps={turbine.turbine_type: None},
         correct_cp_ct_for_tilt=np.array([[False] * N_TURBINES]),
         turbine_type_map=turbine_type_map,
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
         ix_filter=INDEX_FILTER,
     )
 
@@ -371,163 +392,17 @@ def test_axial_induction():
     # Single floating turbine; note that 'tilt_interp' is not set to None
     ai = axial_induction(
         velocities=wind_speed * np.ones((1, 1, 3, 3)),
-        yaw_angle=np.zeros((1, 1)),
-        tilt_angle=np.ones((1, 1)) * 5.0,
-        ref_tilt=np.ones((1, 1)) * 5.0,
-        fCt={turbine.turbine_type: turbine_floating.fCt_interp},
-        tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp},
+        yaw_angles=np.zeros((1, 1)),
+        tilt_angles=np.ones((1, 1)) * 5.0,
+        axial_induction_functions={turbine.turbine_type: turbine.axial_induction_function},
+        tilt_interps={turbine_floating.turbine_type: turbine_floating.tilt_interp},
         correct_cp_ct_for_tilt=np.array([[True]]),
         turbine_type_map=turbine_type_map[0,0],
+        turbine_power_thrust_tables={turbine.turbine_type: turbine.power_thrust_table},
     )
     np.testing.assert_allclose(ai, baseline_ai)
 
 
-def test_rotor_velocity_yaw_correction():
-    N_TURBINES = 4
-
-    wind_speed = average_velocity(10.0 * np.ones((1, 1, 3, 3)))
-    wind_speed_N_TURBINES = average_velocity(10.0 * np.ones((1, N_TURBINES, 3, 3)))
-
-    # Test a single turbine for zero yaw
-    yaw_corrected_velocities = _rotor_velocity_yaw_correction(
-        pP=3.0,
-        yaw_angle=0.0,
-        rotor_effective_velocities=wind_speed,
-    )
-    np.testing.assert_allclose(yaw_corrected_velocities, wind_speed)
-
-    # Test a single turbine for non-zero yaw
-    yaw_corrected_velocities = _rotor_velocity_yaw_correction(
-        pP=3.0,
-        yaw_angle=60.0,
-        rotor_effective_velocities=wind_speed,
-    )
-    np.testing.assert_allclose(yaw_corrected_velocities, 0.5 * wind_speed)
-
-    # Test multiple turbines for zero yaw
-    yaw_corrected_velocities = _rotor_velocity_yaw_correction(
-        pP=3.0,
-        yaw_angle=np.zeros((1, N_TURBINES)),
-        rotor_effective_velocities=wind_speed_N_TURBINES,
-    )
-    np.testing.assert_allclose(yaw_corrected_velocities, wind_speed_N_TURBINES)
-
-    # Test multiple turbines for non-zero yaw
-    yaw_corrected_velocities = _rotor_velocity_yaw_correction(
-        pP=3.0,
-        yaw_angle=np.ones((1, N_TURBINES)) * 60.0,
-        rotor_effective_velocities=wind_speed_N_TURBINES,
-    )
-    np.testing.assert_allclose(yaw_corrected_velocities, 0.5 * wind_speed_N_TURBINES)
-
-
-def test_rotor_velocity_tilt_correction():
-    N_TURBINES = 4
-
-    wind_speed = average_velocity(10.0 * np.ones((1, 1, 3, 3)))
-    wind_speed_N_TURBINES = average_velocity(10.0 * np.ones((1, N_TURBINES, 3, 3)))
-
-    turbine_data = SampleInputs().turbine
-    turbine_floating_data = SampleInputs().turbine_floating
-    turbine = Turbine.from_dict(turbine_data)
-    turbine_floating = Turbine.from_dict(turbine_floating_data)
-    turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type])
-    turbine_type_map = turbine_type_map[None, :]
-
-    # Test single non-floating turbine
-    tilt_corrected_velocities = _rotor_velocity_tilt_correction(
-        turbine_type_map=np.array([turbine_type_map[:, 0]]),
-        tilt_angle=5.0*np.ones((1, 1)),
-        ref_tilt=np.array([turbine.ref_tilt]),
-        pT=np.array([turbine.pT]),
-        tilt_interp={turbine.turbine_type: turbine.tilt_interp},
-        correct_cp_ct_for_tilt=np.array([[False]]),
-        rotor_effective_velocities=wind_speed,
-    )
-
-    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed)
-
-    # Test multiple non-floating turbines
-    tilt_corrected_velocities = _rotor_velocity_tilt_correction(
-        turbine_type_map=turbine_type_map,
-        tilt_angle=5.0*np.ones((1, N_TURBINES)),
-        ref_tilt=np.array([turbine.ref_tilt] * N_TURBINES),
-        pT=np.array([turbine.pT] * N_TURBINES),
-        tilt_interp={turbine.turbine_type: turbine.tilt_interp},
-        correct_cp_ct_for_tilt=np.array([[False] * N_TURBINES]),
-        rotor_effective_velocities=wind_speed_N_TURBINES,
-    )
-
-    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed_N_TURBINES)
-
-    # Test single floating turbine
-    tilt_corrected_velocities = _rotor_velocity_tilt_correction(
-        turbine_type_map=np.array([turbine_type_map[:, 0]]),
-        tilt_angle=5.0*np.ones((1, 1)),
-        ref_tilt=np.array([turbine_floating.ref_tilt]),
-        pT=np.array([turbine_floating.pT]),
-        tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp},
-        correct_cp_ct_for_tilt=np.array([[True]]),
-        rotor_effective_velocities=wind_speed,
-    )
-
-    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed)
-
-    # Test multiple floating turbines
-    tilt_corrected_velocities = _rotor_velocity_tilt_correction(
-        turbine_type_map,
-        tilt_angle=5.0*np.ones((1, N_TURBINES)),
-        ref_tilt=np.array([turbine_floating.ref_tilt] * N_TURBINES),
-        pT=np.array([turbine_floating.pT] * N_TURBINES),
-        tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp},
-        correct_cp_ct_for_tilt=np.array([[True] * N_TURBINES]),
-        rotor_effective_velocities=wind_speed_N_TURBINES,
-    )
-
-    np.testing.assert_allclose(tilt_corrected_velocities, wind_speed_N_TURBINES)
-
-
-def test_compute_tilt_angles_for_floating_turbines():
-    N_TURBINES = 4
-
-    wind_speed = 25.0
-    rotor_effective_velocities = average_velocity(wind_speed * np.ones((1, 1, 3, 3)))
-    rotor_effective_velocities_N_TURBINES = average_velocity(
-        wind_speed * np.ones((1, N_TURBINES, 3, 3))
-    )
-
-    turbine_floating_data = SampleInputs().turbine_floating
-    turbine_floating = Turbine.from_dict(turbine_floating_data)
-    turbine_type_map = np.array(N_TURBINES * [turbine_floating.turbine_type])
-    turbine_type_map = turbine_type_map[None, :]
-
-    # Single turbine
-    tilt = compute_tilt_angles_for_floating_turbines(
-        turbine_type_map=np.array([turbine_type_map[:, 0]]),
-        tilt_angle=5.0*np.ones((1, 1)),
-        tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp},
-        rotor_effective_velocities=rotor_effective_velocities,
-    )
-
-    # calculate tilt again
-    truth_index = turbine_floating_data["floating_tilt_table"]["wind_speed"].index(wind_speed)
-    tilt_truth = turbine_floating_data["floating_tilt_table"]["tilt"][truth_index]
-    np.testing.assert_allclose(tilt, tilt_truth)
-
-    # Multiple turbines
-    tilt_N_turbines = compute_tilt_angles_for_floating_turbines(
-        turbine_type_map=np.array(turbine_type_map),
-        tilt_angle=5.0*np.ones((1, N_TURBINES)),
-        tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp},
-        rotor_effective_velocities=rotor_effective_velocities_N_TURBINES,
-    )
-
-    # calculate tilt again
-    truth_index = turbine_floating_data["floating_tilt_table"]["wind_speed"].index(wind_speed)
-    tilt_truth = turbine_floating_data["floating_tilt_table"]["tilt"][truth_index]
-    np.testing.assert_allclose(tilt_N_turbines, [[tilt_truth] * N_TURBINES])
-
-
 def test_asdict(sample_inputs_fixture: SampleInputs):
 
     turbine = Turbine.from_dict(sample_inputs_fixture.turbine)
@@ -537,44 +412,3 @@ def test_asdict(sample_inputs_fixture: SampleInputs):
     dict2 = new_turb.as_dict()
 
     assert dict1 == dict2
-
-
-def test_simple_cubature():
-
-    # Define a velocity array
-    velocities = np.ones((1, 1, 3, 3))
-
-    # Define sample cubature weights
-    cubature_weights = np.array([1., 1., 1.])
-
-    # Define the axis as last 2 dimensions
-    axis = (velocities.ndim-2, velocities.ndim-1)
-
-    # Calculate expected output based on the given inputs
-    expected_output = 1.0
-
-    # Call the function with the given inputs
-    result = simple_cubature(velocities, cubature_weights, axis)
-
-    # Check if the result matches the expected output
-    np.testing.assert_allclose(result, expected_output)
-
-def test_cubic_cubature():
-
-    # Define a velocity array
-    velocities = np.ones((1, 1, 3, 3))
-
-    # Define sample cubature weights
-    cubature_weights = np.array([1., 1., 1.])
-
-    # Define the axis as last 2 dimensions
-    axis = (velocities.ndim-2, velocities.ndim-1)
-
-    # Calculate expected output based on the given inputs
-    expected_output = 1.0
-
-    # Call the function with the given inputs
-    result = cubic_cubature(velocities, cubature_weights, axis)
-
-    # Check if the result matches the expected output
-    np.testing.assert_allclose(result, expected_output)
diff --git a/tests/turbine_utilities_unit_test.py b/tests/turbine_utilities_unit_test.py
index fb0220b1e..e48b31f45 100644
--- a/tests/turbine_utilities_unit_test.py
+++ b/tests/turbine_utilities_unit_test.py
@@ -18,7 +18,7 @@
 import numpy as np
 import yaml
 
-from floris.tools import build_turbine_dict, check_smooth_power_curve
+from floris.turbine_library import build_cosine_loss_turbine_dict, check_smooth_power_curve
 
 
 def test_build_turbine_dict():
@@ -26,7 +26,6 @@ def test_build_turbine_dict():
     v3_file_path = Path(__file__).resolve().parent / "data" / "nrel_5MW_v3legacy.yaml"
     v4_file_path = Path(__file__).resolve().parent / "data" / "nrel_5MW.yaml"
     test_turb_name = "test_turbine_export"
-    test_file_path = "."
 
     in_dict_v3 = yaml.safe_load( open(v3_file_path, "r") )
 
@@ -37,10 +36,9 @@ def test_build_turbine_dict():
         "thrust_coefficient":in_dict_v3["power_thrust_table"]["thrust"]
     }
 
-    test_dict = build_turbine_dict(
+    test_dict = build_cosine_loss_turbine_dict(
         turbine_data_dict,
         test_turb_name,
-        file_name=os.path.join(test_file_path, test_turb_name+".yaml"),
         generator_efficiency=in_dict_v3["generator_efficiency"],
         hub_height=in_dict_v3["hub_height"],
         pP=in_dict_v3["pP"],
@@ -61,13 +59,16 @@ def test_build_turbine_dict():
     T = 0.5 * in_dict_v3["ref_density_cp_ct"] * (np.pi * in_dict_v3["rotor_diameter"]**2/4) \
         * Ct * ws**2
 
-    # Compare direct computation to those generated by build_turbine_dict
+    # Compare direct computation to those generated by build_cosine_loss_turbine_dict
     assert np.allclose(Ct, test_dict["power_thrust_table"]["thrust_coefficient"])
     assert np.allclose(P/1000, test_dict["power_thrust_table"]["power"])
 
     # Check that dict keys match the v4 structure
     in_dict_v4 = yaml.safe_load( open(v4_file_path, "r") )
     assert set(in_dict_v4.keys()) >= set(test_dict.keys())
+    assert (
+        set(in_dict_v4["power_thrust_table"].keys()) >= set(test_dict["power_thrust_table"].keys())
+    )
 
     # Check thrust conversion from absolute value
     turbine_data_dict = {
@@ -76,18 +77,17 @@ def test_build_turbine_dict():
         "thrust": T/1000
     }
 
-    test_dict_2 = build_turbine_dict(
+    test_dict_2 = build_cosine_loss_turbine_dict(
         turbine_data_dict,
         test_turb_name,
-        file_name=os.path.join(test_file_path, test_turb_name+".yaml"),
         generator_efficiency=in_dict_v4["generator_efficiency"],
         hub_height=in_dict_v4["hub_height"],
-        pP=in_dict_v4["pP"],
-        pT=in_dict_v4["pT"],
+        pP=in_dict_v4["power_thrust_table"]["pP"],
+        pT=in_dict_v4["power_thrust_table"]["pT"],
         rotor_diameter=in_dict_v4["rotor_diameter"],
         TSR=in_dict_v4["TSR"],
-        ref_air_density=in_dict_v4["ref_air_density"],
-        ref_tilt=in_dict_v4["ref_tilt"]
+        ref_air_density=in_dict_v4["power_thrust_table"]["ref_air_density"],
+        ref_tilt=in_dict_v4["power_thrust_table"]["ref_tilt"]
     )
     assert np.allclose(Ct, test_dict_2["power_thrust_table"]["thrust_coefficient"])