tasks/task02.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TDS simulation\n",
    "\n",
    "Thermo-desorption spectra (TDS) are a valuble source of experimental data for understanding the mechanics of hydorgen transport. The nature of the spectra produced by thermo-desorption experiments can give insight to the trapping properties of a given material. There are three stages in a TDS experiment; The implantation stage, the resting stage and the desoption stage. Each stage can be modelled, and the desportion flux spectra reproduced.\n",
    "\n",
    "In this task, we'll simulate a thermo-desorption experiment for a sample of tungsten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import festim as F\n",
    "my_model = F.Simulation()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a mesh from a list of vertices with more refinement close to $x=0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "vertices = np.concatenate([\n",
    "    np.linspace(0, 30e-9, num=200),\n",
    "    np.linspace(30e-9, 3e-6, num=300),\n",
    "    np.linspace(3e-6, 20e-6, num=200),\n",
    "])\n",
    "\n",
    "my_model.mesh = F.MeshFromVertices(vertices)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The material we want to simulate is tungsten. The only property we need is the diffusion coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tungsten = F.Material(\n",
    "    id=1,\n",
    "    D_0=4.1e-07,  # m2/s\n",
    "    E_D=0.39,  # eV\n",
    ")\n",
    "\n",
    "my_model.materials = tungsten"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The source term is defined as:\n",
    "\\begin{equation}\n",
    "    S_{ext} = \\varphi \\cdot f(x) \\quad \\forall \\, t<400 \\text{s}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\varphi =2.5 \\times 10^{19} \\text{m}^{-2}\\text{s}^{-1}$ and $f(x)$ is a Gaussian spatial distribution with a mean value of $4.5 \\: \\text{nm}$ and a width of $2.5 \\: \\text{nm}$.\n",
    "\n",
    "FESTIM has a special class for this case: `ImplantationFlux`.\n",
    "\n",
    "The ion flux is temporally defined using a Sympy Piecewise expression, to be active for the `implantation_time`.\n",
    "\n",
    "Below, `t` is a built-in FESTIM variable that represent time in $\\text{s}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sympy as sp\n",
    "\n",
    "implantation_time = 400  # s\n",
    "\n",
    "ion_flux = sp.Piecewise((2.5e19, F.t <= implantation_time), (0, True))\n",
    "\n",
    "source_term = F.ImplantationFlux(\n",
    "    flux=ion_flux,  # H/m2/s\n",
    "    imp_depth=4.5e-9,  # m\n",
    "    width=2.5e-9,  # m\n",
    "    volume=1\n",
    ")\n",
    "\n",
    "my_model.sources = [source_term]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In FESTIM, each trap is defined by a `Trap` object.\n",
    "Relevent arguments for each trap include:\n",
    "\n",
    "`k_0` and `E_k` are the trapping rate pre-exponential factor in $\\text{m}^{3} \\text{s}^{-1}$ and activation energy in $\\text{eV}$, respectively.\n",
    "\n",
    "`p_0` and `E_p` are the detrapping rate pre-exponential factor in $\\text{s}^{-1}$ and activation energy in $\\text{eV}$, respectively.\n",
    "\n",
    "`materials` is a list of the materials in which the trap is located.\n",
    "Here, it's only `tungsten`.\n",
    "\n",
    "In FESTIM, traps are intrinsic by default. However, traps can also be implemented as extrinsic, as shown in the example hereafter corresponding to 2 intrinsic and 1 extrinsic trap.\n",
    "\n",
    "The time evolution of extrinsic traps density $n_i$ expressed in $\\text{m}^{-3}$ is defined as:\n",
    "\\begin{equation}\n",
    "    \\frac{dn_i}{dt} = \\varphi_0\\:\\left[\\left(1-\\frac{n_i}{n_{a_{max}}}\\right)\\:\\eta_a \\:f_a(x)+\\left(1-\\frac{n_i}{n_{b_{max}}}\\right)\\:\\eta_b \\:f_b(x)\\right]\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_atom_density = 6.3e28  # atom/m3\n",
    "\n",
    "trap_1 = F.Trap(\n",
    "        k_0=4.1e-7/(1.1e-10**2*6*w_atom_density),\n",
    "        E_k=0.39,\n",
    "        p_0=1e13,\n",
    "        E_p=0.87,\n",
    "        density=1.3e-3*w_atom_density,\n",
    "        materials=tungsten\n",
    "    )\n",
    "trap_2 = F.Trap(\n",
    "        k_0=4.1e-7/(1.1e-10**2*6*w_atom_density),\n",
    "        E_k=0.39,\n",
    "        p_0=1e13,\n",
    "        E_p=1.0,\n",
    "        density=4e-4*w_atom_density,\n",
    "        materials=tungsten\n",
    "    )\n",
    "\n",
    "center = 4.5e-9\n",
    "width = 2.5e-9\n",
    "distribution = 1/(width*(2*sp.pi)**0.5) * sp.exp(-0.5*((F.x-center)/width)**2)\n",
    "trap_3 = F.ExtrinsicTrap(\n",
    "        k_0=4.1e-7/(1.1e-10**2*6*w_atom_density),\n",
    "        E_k=0.39,\n",
    "        p_0=1e13,\n",
    "        E_p=1.5,\n",
    "        phi_0=ion_flux,\n",
    "        n_amax=1e-01*w_atom_density, f_a=distribution, eta_a=6e-4,\n",
    "        n_bmax=1e-02*w_atom_density, f_b=sp.Piecewise((1e6, F.x < 1e-6), (0, True)), eta_b=2e-4,\n",
    "        materials=tungsten\n",
    "    )\n",
    "\n",
    "my_model.traps = [trap_1, trap_2, trap_3]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boundary conditions (BCs) can be of several types in FESTIM, the most simple of them being the `DirichletBC` where an analytical expression is given in the argument: `value`. The argument `surfaces` contains a list of all the surfaces on which the BC is applied. If no BC is applied on a surface, it will be considered as a non flux surface (ie $\\frac{\\partial c}{\\partial\\textbf{n}} = 0$).\n",
    "\n",
    "In this case, the solute concentration is set to zero on surfaces 1 and 2 (left and right)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.boundary_conditions = [\n",
    "    F.DirichletBC(surfaces=[1, 2], value=0, field=0)\n",
    "]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the temperature is constant from $t=0$ to $t=450 \\text{s}$ (implantation + resting phase), then increases from $t=450 \\text{s}$ to $t=500 \\text{s}$ in order to perform the thermo-desorption (TDS phase).\n",
    "\n",
    "\\begin{equation}\n",
    "    T(t) =\n",
    "    \\begin{cases}\n",
    "        300, & \\text{if} \\: t < 450 \\\\\n",
    "        300 + 8(t - 450), & \\text{else} \\\\\n",
    "    \\end{cases}\n",
    "\\end{equation}\n",
    "\n",
    "$T$ is expressed in $\\text{K}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "implantation_temp = 300  # K\n",
    "temperature_ramp = 8  # K/s\n",
    "\n",
    "start_tds = implantation_time + 50  # s\n",
    "\n",
    "my_model.T = sp.Piecewise(\n",
    "        (implantation_temp, F.t < start_tds),\n",
    "        (implantation_temp + temperature_ramp*(F.t-start_tds), True))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Stepsize` object defines the simulation stepsize.\n",
    "\n",
    "The argument `initial_value` is the initial stepsize is expressed in $\\text{s}$.\n",
    "\n",
    "An adaptive stepsize algorithm has been implemented in order to save computational cost.\n",
    "`stepsize_change_ratio` defines by how much the stepsize is increased or decreased after each iteration (depending on the number of Newton iterations required to converged).\n",
    "`dt_min` is the lower limit for the stepsize below which the computation will stop.\n",
    "\n",
    "The optional argument `max_stepsize` sets the maximum stepsize. It takes a function of time. In this case, we cap the stepsize at 0.5 s after the start of the TDS.\n",
    "We also set `milestones` in order to make sure the simulation doesn't over step the end of the implantation and the start of the TDS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.dt = F.Stepsize(\n",
    "    initial_value=0.5,\n",
    "    stepsize_change_ratio=1.1,\n",
    "    max_stepsize=lambda t: 0.5 if t > start_tds else None,\n",
    "    dt_min=1e-05,\n",
    "    milestones=[implantation_time, start_tds],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.settings = F.Settings(\n",
    "    absolute_tolerance=1e10,\n",
    "    relative_tolerance=1e-09,\n",
    "    final_time=500\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to plot the evolution of the surface fluxes as a function of time.\n",
    "\n",
    "To do so, we'll use 'derived quantities' objects.\n",
    "There is a wide range of derived quantities available in FESTIM.\n",
    "\n",
    "Here, we'll use `TotalVolume` (volume integration) and `HydrogenFlux`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_derived_quantities = [\n",
    "        F.TotalVolume(\"solute\", volume=1),\n",
    "        F.TotalVolume(\"retention\", volume=1),\n",
    "        F.TotalVolume(\"1\", volume=1),\n",
    "        F.TotalVolume(\"2\", volume=1),\n",
    "        F.TotalVolume(\"3\", volume=1),\n",
    "        F.HydrogenFlux(surface=1),\n",
    "        F.HydrogenFlux(surface=2)\n",
    "    ]\n",
    "\n",
    "derived_quantities = F.DerivedQuantities(\n",
    "    list_of_derived_quantities,\n",
    "    # filename=\"tds/derived_quantities.csv\"  # optional set a filename to export the data to csv\n",
    "    show_units=True,\n",
    ")\n",
    "\n",
    "\n",
    "my_model.exports = [derived_quantities]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Defining initial values\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Defining variational problem\n",
      "Defining source terms\n",
      "Defining boundary conditions\n",
      "Time stepping...\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "Calling FFC just-in-time (JIT) compiler, this may take some time.\n",
      "100.0 %        5.0e+02 s    Elapsed time so far: 7.3 s\n"
     ]
    }
   ],
   "source": [
    "my_model.initialise()\n",
    "my_model.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = derived_quantities.t\n",
    "flux_left = derived_quantities.filter(fields=\"solute\", surfaces=1).data\n",
    "flux_right = derived_quantities.filter(fields=\"solute\", surfaces=2).data\n",
    "\n",
    "flux_total = -np.array(flux_left) - np.array(flux_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time (s)')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(t, flux_total, linewidth=3)\n",
    "\n",
    "plt.ylabel(r\"Desorption flux (m$^{-2}$ s$^{-1}$)\")\n",
    "plt.xlabel(r\"Time (s)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make use of numpy to compute the time derivative of the traps inventories!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "trap_1 = derived_quantities.filter(fields=\"1\").data\n",
    "trap_2 = derived_quantities.filter(fields=\"2\").data\n",
    "trap_3 = derived_quantities.filter(fields=\"3\").data\n",
    "\n",
    "contribution_trap_1 = -np.diff(trap_1)/np.diff(t)\n",
    "contribution_trap_2 = -np.diff(trap_2)/np.diff(t)\n",
    "contribution_trap_3 = -np.diff(trap_3)/np.diff(t)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the TDS spectrum with the 3 traps contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time (s)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, flux_total, linewidth=3)\n",
    "plt.plot(t[1:], contribution_trap_1, linestyle=\"--\", color=\"grey\")\n",
    "plt.fill_between(t[1:], 0, contribution_trap_1, facecolor='grey', alpha=0.1)\n",
    "plt.plot(t[1:], contribution_trap_2, linestyle=\"--\", color=\"grey\")\n",
    "plt.fill_between(t[1:], 0, contribution_trap_2, facecolor='grey', alpha=0.1)\n",
    "plt.plot(t[1:], contribution_trap_3, linestyle=\"--\", color=\"grey\")\n",
    "plt.fill_between(t[1:], 0, contribution_trap_3, facecolor='grey', alpha=0.1)\n",
    "\n",
    "plt.xlim(450, 500)\n",
    "plt.ylim(bottom=-1.25e18, top=0.6e19)\n",
    "plt.ylabel(r\"Desorption flux (m$^{-2}$ s$^{-1}$)\")\n",
    "plt.xlabel(r\"Time (s)\")\n",
    "\n",
    "plt.ylabel(r\"Desorption flux (m$^{-2}$ s$^{-1}$)\")\n",
    "plt.xlabel(r\"Time (s)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task:\n",
    "1) Increase the implantation temperature to 500 K and see how the TDS spectrum is affected\n",
    "2) Vary the detrapping energy of the first trap\n",
    "3) At the end of the implantation phase, what is the proportion of hydrogen trapped in trap 2?\n",
    "\n",
    "    <details>\n",
    "    <summary>Show solution</summary>\n",
    "    <br>\n",
    "    Make use of the exported derived quantities.\n",
    "\n",
    "    We computed the hydrogen inventory in traps 1, 2 and 3 as well as the mobile inventory.\n",
    "\n",
    "    ```python\n",
    "    t = derived_quantities.t\n",
    "    trap_1 = derived_quantities.filter(fields=\"1\").data\n",
    "    trap_2 = derived_quantities.filter(fields=\"2\").data\n",
    "    trap_3 = derived_quantities.filter(fields=\"3\").data\n",
    "    mobile = derived_quantities.filter(fields=\"solute\", instances=F.TotalVolume).data\n",
    "    total = derived_quantities.filter(fields=\"retention\").data\n",
    "\n",
    "    plt.stackplot(t, trap_1, trap_2, trap_3, mobile)\n",
    "    ```\n",
    "\n",
    "    Try to run it!\n",
    "\n",
    "    </details>\n"
   ]
  }
 ],
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