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skinfriction_subplots.py
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skinfriction_subplots.py
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# Python packages
import random, os, sys, datetime
import time as sys_time
import numpy as np
from matplotlib import pyplot as plt
import skfmm
#import importlib.util
#sys.path.append(os.path.abspath(os.path.join(os.getcwd(), './../')))
from scipy import spatial
from skimage.util.shape import view_as_windows
from scipy import interpolate
import pickle
# Pytorch imports
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.modules.loss as Loss
import torch.nn.functional as F
# Helpers Import
from torch.utils.data import DataLoader
#--------- Project Imports ----------#
from DfpNet import TurbNetG
from dataset import TurbDataset
import utils
from utils import log
################################################################################
#
# Function to read grid
#
################################################################################
def read_grid(fname):
# Open grid file
f = open(fname)
# Read imax, jmax
# 3D grid specifies number of blocks on top line
line1 = f.readline()
flag = len(line1.split())
if flag == 1:
threed = True
else:
threed = False
if threed:
line1 = f.readline()
imax, kmax, jmax = [int(x) for x in line1.split()]
else:
imax, jmax = [int(x) for x in line1.split()]
kmax = 1
# Read geometry data
x = np.zeros((imax,jmax))
y = np.zeros((imax,jmax))
if threed:
for j in range(0, jmax):
for k in range(0, kmax):
for i in range(0, imax):
x[i,j] = float(f.readline())
for j in range(0, jmax):
for k in range(0, kmax):
for i in range(0, imax):
dummy = float(f.readline())
for j in range(0, jmax):
for k in range(0, kmax):
for i in range(0, imax):
y[i,j] = float(f.readline())
else:
for j in range(0, jmax):
for i in range(0, imax):
x[i,j] = float(f.readline())
for j in range(0, jmax):
for i in range(0, imax):
y[i,j] = float(f.readline())
# Print message
print('Successfully read grid file ' + fname)
# Close the file
f.close
return (imax, jmax, kmax, x, y, threed)
#files=[]
TEST_DEBUG=True
#liwei_plt_cm = plt.cm.magma
liwei_plt_cm = plt.cm.jet
#liwei_plt_cm = plt.cm.Spectral
error_plt_cm = plt.cm.magma
##folder="./test/"
#files = os.listdir(folder)
#files.sort()
#
def main():
folder="./BASIC_data_coordinates_final_metricsAll_1940/test_avg/"
#folder="../../BASIC_data_coordinates_final_v3/test/"
files = os.listdir(folder)
files.sort()
#files.append("naca0012_50_221_945.npz")
modelPath="./"
channelExpo=7
modelName = "modelG"
cuda = torch.device('cuda') # Default CUDA device
with open(modelPath+'/max_inputs.pickle', 'rb') as f: max_inputs = pickle.load(f)
f.close()
with open(modelPath+'/max_targets.pickle', 'rb') as f: max_targets = pickle.load(f)
f.close()
print("## max inputs ##: ",max_inputs)
print("## max targets ##: ",max_targets)
with open(modelPath+'/min_inputs.pickle', 'rb') as f: min_inputs = pickle.load(f)
f.close()
with open(modelPath+'/min_targets.pickle', 'rb') as f: min_targets = pickle.load(f)
f.close()
print("## min inputs ##: ",min_inputs)
print("## min targets ##: ",min_targets)
nsteps = 4
max_targets = np.asarray(max_targets)
min_targets = np.asarray(min_targets)
###############################################################################
netG = TurbNetG(channelExponent=channelExpo)
netG.load_state_dict( torch.load(modelPath+'/'+modelName, map_location='cpu') )
#if torch.cuda.is_available:
netG.cuda()
netG.eval()
#plt.figure()
start_time_main_loop = sys_time.time()
#for it in range(0, 1):
for n in range(len(files)):
# n= it
data=np.load(folder+files[n])['a']
basename = files[n].split("_")[0]
print(basename)
#test_mesh_folder = "../../DATASET/test_mesh/"
# imax, jmax, kmax, x, y, threed = read_grid(test_mesh_folder+basename+".p3d")
test_mesh_folder = "./test_mesh/"
imax, jmax, kmax, x, y, threed = read_grid(test_mesh_folder+basename+".p3d")
xmach = data[0,:,:]
aoa = data[1,:,:]
re = data[2,:,:]
si4 = data[3]
sj1 = data[4,:,:]
sj3 = data[5,:,:]
sj4 = data[6]
sk1 = data[7,:,:]
sk3 = data[8,:,:]
sk4 = data[9]
xin = data[14,:,:]
yin = data[15,:,:]
p_data = data[13,:,:]**2 * data[10,:,:] / 1.4
xm_inf = xmach[0,0]
aoa_inf = aoa[64,-1]
re_inf = re[0,0]
#rho_inf = np.mean(data[10,:,-1]) #rho
#p_inf = np.mean(p_data[:,-1])
print("Reynolds & Mach number:",re_inf, xm_inf)
mach_data = np.sqrt(data[11]**2+data[12]**2)/data[13]
pt_data = p_data*(1+0.2*mach_data**2)**3.5
p_inf = np.sum(p_data[:,-1]*sk4[:,-1])/np.sum(sk4[:,-1])
pt_inf = np.sum(pt_data[:,-1]*sk4[:,-1])/np.sum(sk4[:,-1])
rho_inf = np.sum(data[10,:,-1]*sk4[:,-1])/np.sum(sk4[:,-1])
print(p_inf, pt_inf)
data[13,:,:] = p_data[:,:]
#u_inf = data[11,64,-1] #u
#v_inf = data[12,64,-1] #v
a_inf = np.mean(data[13,:,-1]) #a
#xm_inf = (u_inf**2+v_inf**2)**0.5/a_inf
#re_inf = re[64,-1]
xmach = torch.from_numpy(xmach).type(torch.FloatTensor).cuda()
aoa = torch.from_numpy(aoa).type(torch.FloatTensor).cuda()
re = torch.from_numpy(re).type(torch.FloatTensor).cuda()
si4 = torch.from_numpy(si4).type(torch.FloatTensor).cuda()
sj1 = torch.from_numpy(sj1).type(torch.FloatTensor).cuda()
sj3 = torch.from_numpy(sj3).type(torch.FloatTensor).cuda()
sj4 = torch.from_numpy(sj4).type(torch.FloatTensor).cuda()
sk1 = torch.from_numpy(sk1).type(torch.FloatTensor).cuda()
sk3 = torch.from_numpy(sk3).type(torch.FloatTensor).cuda()
sk4 = torch.from_numpy(sk4).type(torch.FloatTensor).cuda()
xin = torch.from_numpy(xin).type(torch.FloatTensor).cuda()
yin = torch.from_numpy(yin).type(torch.FloatTensor).cuda()
xmach =(xmach- min_inputs[0])/(max_inputs[0]-min_inputs[0]+1e-20)
aoa = ( aoa- min_inputs[1])/(max_inputs[1]-min_inputs[1])
re = ( re- min_inputs[2])/(max_inputs[2]-min_inputs[2]) #/100 #.... fsX=10
si4 = ( si4- min_inputs[3])/(max_inputs[3]-min_inputs[3]) #/100 #.... fsX=10
sj1 =( sj1 - min_inputs[4])/(max_inputs[4]-min_inputs[4])
sj3 =( sj3 - min_inputs[5])/(max_inputs[5]-min_inputs[5])
sj4 =( sj4 - min_inputs[6])/(max_inputs[6]-min_inputs[6])
sk1 =( sk1 - min_inputs[7])/(max_inputs[7]-min_inputs[7])
sk3 =(sk3 - min_inputs[8])/(max_inputs[8]-min_inputs[8])
sk4 =(sk4 - min_inputs[9])/(max_inputs[9]-min_inputs[9])
xin =(xin - min_inputs[10])/(max_inputs[10]-min_inputs[10])
yin =(yin - min_inputs[11])/(max_inputs[11]-min_inputs[11])
# print(max_inputs)
#print(xmach[0,0].item(), aoa[0,0].item(), re[0,0].item())
#input_gpu = torch.from_numpy(input).cuda()
input_gpu = torch.cat( (xmach, aoa, re, si4, sj1, sj3, sj4, sk1, sk3, sk4, xin, yin) )
input_gpu = input_gpu.view((1, 12, aoa.shape[0], aoa.shape[1]))
#input = np.zeros((1, 3, binaryMaskInv.shape[0],binaryMaskInv.shape[1]))
##input[0, 0:,] = channelfsX
##input[0, 1:,] = channelfsY
##input[0, 2:,] = binaryMaskInv
#input[0, 0,] = channelfsX
#input[0, 1,] = channelfsY
#input[0, 2,] = binaryMaskInv
#input = self._getInputArray(numpyMask)
#plt.figure()
#plt.imshow(channelfsX.detach().cpu().numpy()) #, levels)
#plt.figure()
#plt.imshow(channelfsY.detach().cpu().numpy()) #, levels)
#plt.show()
# if TEST_DEBUG:
# for i in range(3):
# plt.subplot(1,3, i+1)
# plt.imshow(input_gpu[0][i].detach().cpu().numpy())
# # as it is a variable that requires grad, need to detach(); also cannot convert a cuda var to numpy
# plt.colorbar()
# plt.show()
#fullSolution = netG(torch.Tensor(input)).detach().numpy()
fullSolution = netG(input_gpu)
fullSolution = fullSolution.detach().cpu().numpy()
for i in range(4):
fullSolution[0][i] = fullSolution[0][i]*(max_targets[i]-min_targets[i])+min_targets[i]
p_Solution = fullSolution[0][3]**2 * (fullSolution[0][0]) / 1.4
fullSolution[0][3] = p_Solution
#plt.subplot(1,1,1,aspect=1.)
#plt.figure()
fig, ax = plt.subplots()
fig.set_size_inches(6,4)
#if False:
# i = 3
# vmin=np.min(data[i+10])
# vmax=np.max(data[i+10])
# lv = np.r_[vmin: vmax: 9j*5]
# plt.subplot(1,1, 1, aspect=1.)
# plt.contourf(x, y, data[i+10], lv, cmap=liwei_plt_cm)
# plt.xlim(-0.5,1.5)
# plt.ylim(-0.5,1.5)
# plt.colorbar(ticks=lv[0::nsteps])
kw = 0
jsta=25 #26 # node point 25 to 105
jend=104
#npOutput[4,:,:] = values[1]*values[3]/values[0] #sj1*sj4/vol
#npOutput[5,:,:] = values[2]*values[3]/values[0] #sj3*sj4/vol
#npOutput[7,:,:] = values[4]*values[6]/values[0] #sk1*sk4/vol
#npOutput[8,:,:] = values[5]*values[6]/values[0] #sk3*sk4/vol
si4 = data[3]
sj1 = data[4,:,:]
sj3 = data[5,:,:]
sj4 = data[6]
sk1 = data[7,:,:]
sk3 = data[8,:,:]
sk4 = data[9]
sj1 = sj1*sj4/si4
sj3 = sj3*sj4/si4
sk1 = sk1*sk4/si4
sk3 = sk3*sk4/si4
# vor = d(w)/dx - d(u)/dz # in eta zeta
# = d(w)/deta * eta_x + d(w)/dzeta * zeta_x
# -(d(u)/deta * eta_z + d(u)/dzeta * zeta_z
# = (w[j,k] - w[j-1,k]) * sj1 + (w[j,k]-w[j,k-1]) * sk1
# -(u[j,k] - u[j-1,k]) * sj3 - (u[j,k]-u[j,k-1]) * sk3
def cal_vorticity(sj1, sj3, sk1, sk3, u, w):
jdim, kdim = 128, 128
vorticity = np.copy(u)
for j in range(1, jdim):
for k in range(1, kdim):
vorticity[j,k] = (w[j,k] - w[j-1,k]) * sj1[j,k] + (w[j,k]-w[j,k-1]) * sk1[j,k] -(u[j,k] - u[j-1,k]) * sj3[j,k] - (u[j,k]-u[j,k-1]) * sk3[j,k]
if k==0:
vorticity[j,k] = (w[j,k] - w[j-1,k]) * sj1[j,k] + (w[j,k+1]-w[j,k]) * sk1[j,k] -(u[j,k] - u[j-1,k]) * sj3[j,k] - (u[j,k+1]-u[j,k]) * sk3[j,k]
if j==0:
for k in range(1, kdim):
vorticity[j,k] = (w[j+1,k] - w[j,k]) * sj1[j,k] + (w[j,k]-w[j,k-1]) * sk1[j,k] -(u[j+1,k] - u[j,k]) * sj3[j,k] - (u[j,k]-u[j,k-1]) * sk3[j,k]
if k==0:
vorticity[j,k] = (w[j+1,k] - w[j,k]) * sj1[j,k] + (w[j,k+1]-w[j,k]) * sk1[j,k] -(u[j+1,k] - u[j,k]) * sj3[j,k] - (u[j,k+1]-u[j,k]) * sk3[j,k]
return vorticity
# nxvor
# ex ey ez
# nx 0 nz --- nx=sk1; nz=sk3
# 0 omega 0
# = [-omega*nz
# 0
# omega*nx]
# shear stress abs:
#|| tau_w || = sqrt(omeaga**2 * nz**2 + omega**2)
# um = 0.5*(u[:,kw]+u[:,kw+1])
# wm = 0.5*(w[:,kw]+w[:,kw+1])
# tangent_x = um/sqrt(um*um + wm*wm)
# tangent_z = wm/sqrt(um*um + wm*wm)
# tau_w = -omega*nz *tangent_x + omega*nx *tangent_y
# tau_w = -omega*sk3*tangent_x + omega*sk1*tangent_y
#vorticitySolution = cal_vorticity(sj1*sj4/si4, sj3*sj4/si4, sk1*sk4/si4, sk3*sk4/si4, fullSolution[0][1], fullSolution[0][2])
#vorticity = cal_vorticity(sj1*sj4/si4, sj3*sj4/si4, sk1*sk4/si4, sk3*sk4/si4, data[11], data[12])
vorticitySolution = cal_vorticity(sj1, sj3, sk1, sk3, fullSolution[0][1], fullSolution[0][2])
vorticity = cal_vorticity(sj1, sj3, sk1, sk3, data[11], data[12])
jdim = 128
tangent_x = np.zeros(jdim)
tangent_y = np.zeros(jdim)
normal_x = np.zeros(jdim)
normal_y = np.zeros(jdim)
for j in range(jsta, jend):
t1 = x[j+1,0]-x[j,0]
t2 = y[j+1,0]-y[j,0]
tangent_mag = t1**2+t2**2
tangent_mag = tangent_mag**0.5
tangent_x[j] = t1 / (1e-10+tangent_mag)
tangent_y[j] = t2 / (1e-10+tangent_mag)
n1 = x[j,1]-x[j,0]
n2 = y[j,1]-y[j,0]
normal_mag = n1**2+n2**2
normal_mag = normal_mag**0.5
normal_x[j] = n1 / (1e-10+normal_mag)
normal_y[j] = n2 / (1e-10+normal_mag)
#t_x_Sol, t_y_Sol = cal_tangent(fullSolution[0][1], fullSolution[0][2])
#t_x, t_y = cal_tangent(data[11], data[12])
#tau_w_Sol = vorticitySolution * (-sk3 * t_x_Sol + sk1 * t_y_Sol)
#tau_w = vorticity * (-sk3 * t_x + sk1 * t_y )
#plt.plot(x[jsta:jend,kw], (fullSolution[0,3,jsta:jend,kw]-1/1.4)/0.5/xm_inf/xm_inf/rho_inf, "k-")
#plt.plot(x[jsta:jend,kw], ( p_data[jsta:jend,kw]-1/1.4)/0.5/xm_inf/xm_inf/rho_inf, "g+")
#plt.plot(x[jsta:jend,kw], (fullSolution[0,3,jsta:jend,kw]-p_inf)/0.5/xm_inf/xm_inf/rho_inf, "k-")
#plt.plot(x[jsta:jend,kw], ( p_data[jsta:jend,kw]-p_inf)/0.5/xm_inf/xm_inf/rho_inf, "g+")
#plt.plot(x[jsta:jend,kw], re_inf* np.abs(vorticitySolution[jsta:jend,kw] )/(pt_inf-p_inf), "k-")
#plt.plot(x[jsta:jend,kw], re_inf* np.abs( vorticity[jsta:jend,kw] )/(pt_inf-p_inf), "g+")
#ax.plot( x[jsta:jend,kw], xm_inf/re_inf/1e6*(tau_w_Sol[jsta:jend,kw])/(pt_inf-p_inf), "k-")
#ax.scatter(x[jsta:jend,kw], xm_inf/re_inf/1e6*( tau_w[jsta:jend,kw])/(pt_inf-p_inf), s=80, facecolors='none', edgecolors='r')
kw=1
# ax.plot( x[jsta:jend,kw], vorticitySolution[jsta:jend,kw], "k-")
# ax.scatter(x[jsta:jend,kw], vorticity[jsta:jend,kw], s=80, facecolors='none', edgecolors='r')
ax.plot( x[jsta:jend,kw], xm_inf/re_inf/1e6*vorticitySolution[jsta:jend,kw]*( -normal_y[jsta:jend]*tangent_x[jsta:jend]+normal_x[jsta:jend]*tangent_y[jsta:jend] )/(pt_inf-p_inf), "r-", label="DNN")
ax.scatter(x[jsta:jend,kw], xm_inf/re_inf/1e6*vorticity[jsta:jend,kw]*( -normal_y[jsta:jend]*tangent_x[jsta:jend]+normal_x[jsta:jend]*tangent_y[jsta:jend] )/(pt_inf-p_inf), s=80, facecolors='none', edgecolors='k', label="CFD")
#ax.plot( x[jsta:jend,kw], xm_inf/re_inf/1e6*vorticitySolution[jsta:jend,kw]*( -normal_y[jsta:jend]*tangent_x[jsta:jend]+normal_x[jsta:jend]*tangent_y[jsta:jend] )/0.5/xm_inf/xm_inf/rho_inf, "k-")
#ax.scatter(x[jsta:jend,kw], xm_inf/re_inf/1e6*vorticity[jsta:jend,kw]*( -normal_y[jsta:jend]*tangent_x[jsta:jend]+normal_x[jsta:jend]*tangent_y[jsta:jend] )/0.5/xm_inf/xm_inf/rho_inf, s=80, facecolors='none', edgecolors='r')
# ax.plot( x[jsta:jend,kw], (sk3[jsta:jend,kw]), "k-")
# ax.scatter(x[jsta:jend,kw], (sk1[jsta:jend,kw]), s=80, facecolors='none', edgecolors='r')
#ax.plot( x[jsta:jend,kw], re_inf*0.5*np.abs(vorticitySolution[jsta:jend,kw]+vorticitySolution[jsta-1:jend-1,kw])/(pt_inf-p_inf), "k-")
#ax.scatter(x[jsta:jend,kw], re_inf*0.5*np.abs( vorticity[jsta:jend,kw]+ vorticity[jsta-1:jend-1,kw])/(pt_inf-p_inf), s=80, facecolors='none', edgecolors='r')
#ax.plot(x[jsta:jend,kw], re_inf*0.5*np.abs( vorticity[jsta:jend,kw]+ vorticity[jsta-1:jend-1,kw])/(pt_inf-p_inf), "k+")
ax.set_xlim(-0.1,1.1)
ax.set_ylim(-0.01, 0.015)
sup_name = files[n].split("_")
ax.set_title(sup_name[0]+", Ma="+ str(float(sup_name[1])/100)+", AoA="+str(float(sup_name[2])/100)+r"$^{\circ}$, Re="+sup_name[3].split(".")[0]+"k")
if "e221" in sup_name[0]:
plt.legend()
#plt.tight_layout()
u = fullSolution[0][1]
#u = p_Solution
var_string = "x"
vmin=np.min(u)
vmax=np.max(u)
lv = np.r_[vmin: vmax: 9j*5]
nsteps = 4
# airfoil_x = data[14,jsta:jend,0]
# airfoil_y = data[15,jsta:jend,0]
#
#
# #ins = ax.inset_axes([0.7,0.8,0.2,0.2])
# #ins = ax.inset_axes([0.0,0.0,1.,1.])
# if aoa_inf > 0:
# ins = ax.inset_axes([0.4, 0.7, 0.4, 0.4])
# #ins = ax.inset_axes([0.4, 0., 0.4, 0.4])
# else:
# ins = ax.inset_axes([0.4, 0.7, 0.4, 0.4])
# #ins = ax.inset_axes([0.4, 0., 0.4, 0.4])
#
# ins.set_aspect(1.)
# ins.plot(airfoil_x, airfoil_y, color='grey')
#
# # Turn off tick labels
# ins.set_yticklabels([])
# ins.set_xticklabels([])
# ins.axis('off')
#plt.show()
# if aoa_inf>0:
# ins = ax.inset_axes([0.5, 0.0, 0.4, 0.4])
# else:
# ins = ax.inset_axes([0.5, 0.6, 0.4, 0.4])
ins = ax.inset_axes([0.5, 0.6, 0.4, 0.4])
#ins.set_aspect(1.)
ins.set_aspect(1.)
#ins.patch.set_facecolor('white')
ins.contourf(x, y, u, lv, cmap=liwei_plt_cm)
ins.set_xlim(-0.2,1.2)
if "e473" in sup_name[0]:
ins.set_ylim(-0.6,0.7)
else:
ins.set_ylim(-0.2,1.2)
ins.axis('off')
ins.set_facecolor('#ffffff')
plt.show()
#fig.savefig('./results_test_skinfriction/vort_'+files[n][:-4]+'.eps', format='eps', dpi=300)
#fig.savefig('./results_test_skinfriction/vort_'+files[n][:-4]+'.png', format='png', dpi=300)
#plt.ylim(0.55,0.9)
#plt.show()
end_time_main_loop = sys_time.time()
#print("Main loop execution time: {}".format(convertSecond(end_time_main_loop - start_time_main_loop)))
#plt.figure()
#plt.plot(time,history)
#plt.savefig("./figures/loss_history.png")
if __name__ == '__main__':
start = sys_time.time()
main()
end = sys_time.time()
#logMsg("\tExecution time: {}".format(convertSecond(end - start)))
#print("Execution time: {}".format(convertSecond(end - start)))