g2.AddInputData(output) g2.Update() sphere = dsa.CompositeDataSet(g2.GetOutput()) vn = algs.vertex_normal(sphere) compare(algs.mag(vn) - 1, 1E-6) sn = algs.surface_normal(sphere) compare(algs.mag(sn) - 1, 1E-6) dot = algs.dot(vn, vn) assert dot.DataSet is sphere compare(dot - 1, 1E-6) assert algs.all(algs.cross(vn, vn) == [0, 0, 0]) fd = sphere.FieldData['field array'] assert algs.all(fd == 5) assert algs.shape(fd) == (22,) assert vn.DataSet is sphere # -------------------------------------- na = dsa.NoneArray # Test operators assert (1 + na - 1 - randomVec) is na # Test slicing and indexing
g2.AddInputData(output) g2.Update() sphere = dsa.CompositeDataSet(g2.GetOutput()) vn = algs.vertex_normal(sphere) assert algs.all(algs.mag(vn) - 1 < 1E-6) sn = algs.surface_normal(sphere) assert algs.all(algs.mag(sn) - 1 < 1E-6) dot = algs.dot(vn, vn) assert dot.DataSet is sphere assert algs.all(dot == 1) assert algs.all(algs.cross(vn, vn) == [0, 0, 0]) fd = sphere.FieldData['field array'] assert algs.all(fd == 5) assert algs.shape(fd) == (22,) assert vn.DataSet is sphere # -------------------------------------- na = dsa.NoneArray # Test operators assert (1 + na - 1 - randomVec) is na # Test slicing and indexing
radius = algs.sqrt(x**2 + y**2)
theta = algs.arctan2(y, x)
# Cylindrical Direction Vectors
# radVec
radVec = coordVec.copy()
radVec[:, 2] = radVec[:, 2] * 0
radVec = algs.norm(radVec)
# zVec
zVec = np.repeat([[0, 0, 1]], radVec[:, 0].size, axis=0)
zVec = algs.make_vector(*zVec.T)
# thetaVec
thetaVec = algs.cross(zVec, radVec)
thetaVec = algs.make_vector(*thetaVec.T)
thetaVec = algs.norm(thetaVec)
############################################
# CREATING CARTESIAN VECTOR DATA
############################################
VelMeanVec = algs.make_vector(input0.PointData['Mean_X_Velocity'].Arrays[0],
input0.PointData['Mean_Y_Velocity'].Arrays[0],
input0.PointData['Mean_Z_Velocity'].Arrays[0])
VelRMSEVec = algs.make_vector(input0.PointData['RMSE_X_Velocity'].Arrays[0],
input0.PointData['RMSE_Y_Velocity'].Arrays[0],
input0.PointData['RMSE_Z_Velocity'].Arrays[0])
############################################
def make_features(rans_vtk, Ls=1, Us=1, ros=1, nondim='local'):
from cfd2ml.utilities import build_cevm
small = np.cbrt(np.finfo(float).tiny)
Ps = 0.5 * ros * Us**2
rans_nnode = rans_vtk.number_of_points
delij = np.zeros([rans_nnode, 3, 3])
for i in range(0, 3):
delij[:, i, i] = 1.0
# Wrap vista object in dsa wrapper
rans_dsa = dsa.WrapDataObject(rans_vtk)
print('Feature:')
nfeat = 15
feat = 0
q = np.empty([rans_nnode, nfeat])
feature_labels = np.empty(nfeat, dtype='object')
# Feature 1: non-dim Q-criterion
################################
print('1: non-dim Q-criterion...')
# Velocity vector
U = rans_dsa.PointData[
'U'] # NOTE - Getting variables from dsa obj not vtk obj as want to use algs etc later
# Velocity gradient tensor and its transpose
# J[:,i-1,j-1] is dUidxj
# Jt[:,i-1,j-1] is dUjdxi
Jt = algs.gradient(U) # Jt is this one as algs uses j,i ordering
J = algs.apply_dfunc(np.transpose, Jt, (0, 2, 1))
# Strain and vorticity tensors
Sij = 0.5 * (J + Jt)
Oij = 0.5 * (J - Jt)
# Frob. norm of Sij and Oij (Snorm and Onorm are actually S^2 and O^2, sqrt needed to get norms)
Snorm = algs.sum(2.0 * Sij**2, axis=1) # sum i axis
Snorm = algs.sum(Snorm,
axis=1) # sum previous summations i.e. along j axis
Onorm = algs.sum(2.0 * Oij**2, axis=1) # sum i axis
Onorm = algs.sum(Onorm,
axis=1) # sum previous summations i.e. along j axis
# Store q1
q[:, feat] = (Onorm - 0.5 * Snorm) / (Onorm + 0.5 * Snorm + small)
feature_labels[feat] = 'Normalised strain'
feat += 1
# clean up
Snorm = algs.sqrt(Snorm) #revert to revert to real Snorm for use later
Onorm = algs.sqrt(Onorm) #revert to revert to real Onorm for use later
# Feature 2: Turbulence intensity
#################################
print('2: Turbulence intensity')
tke = rans_dsa.PointData['k']
UiUi = algs.mag(U)**2.0
# q[:,feat] = tke/(0.5*UiUi+tke+small)
q[:, feat] = tke / (0.5 * UiUi + small)
feature_labels[feat] = 'Turbulence intensity'
feat += 1
# Feature 3: Turbulence Reynolds number
#######################################
print('3: Turbulence Reynolds number')
nu = rans_dsa.PointData['mu_l'] / rans_dsa.PointData['ro']
Red = (algs.sqrt(tke) * rans_dsa.PointData['d']) / (50.0 * nu)
q[:, feat] = algs.apply_dfunc(np.minimum, Red, 2.0)
#Red = 0.09**0.25*algs.sqrt(tke)*rans_dsa.PointData['d']/nu
#q[:,feat] = algs.apply_dfunc(np.minimum, Red, 100.0)
feature_labels[feat] = 'Turbulence Re'
feat += 1
# Feature 4: Pressure gradient along streamline
###############################################
print('4: Stream-wise pressure gradient')
A = np.zeros(rans_nnode)
B = np.zeros(rans_nnode)
dpdx = algs.gradient(rans_dsa.PointData['p'])
ro = rans_dsa.PointData['ro']
Umag = algs.mag(U)
for k in range(0, 3):
A += U[:, k] * dpdx[:, k]
if nondim == 'global':
A = A / Umag
q[:, feat] = A * Ls / Ps
elif nondim == 'local':
for i in range(0, 3):
for j in range(0, 3):
B += U[:, i] * U[:, i] * dpdx[:, j] * dpdx[:, j]
q[:, feat] = A / (algs.sqrt(B) + algs.abs(A) + small)
feature_labels[feat] = 'Stream-wise Pgrad'
feat += 1
# Feature 5: Ratio of turb time scale to mean strain time scale
###############################################################
print('5: Ratio of turb time scale to mean strain time scale')
# A = 1.0/rans_dsa.PointData['w'] #Turbulent time scale (eps = k*w therefore also A = k/eps)
# B = 1.0/Snorm
# q[:,feat] = A/(A+B+small)
q[:, feat] = Snorm / (rans_dsa.PointData['w'] + small)
feature_labels[feat] = 'turb/strain time-scale'
feat += 1
# Feature 6: Viscosity ratio
############################
print('6: Viscosity ratio')
nu_t = rans_dsa.PointData['mu_t'] / ro
# q[:,feat] = nu_t/(100.0*nu + nu_t)
q[:, feat] = nu_t / (nu + small)
feature_labels[feat] = 'Viscosity ratio'
feat += 1
# Feature 7: Vortex stretching
##############################
print('7: Vortex stretching')
A = np.zeros(rans_nnode)
B = np.zeros(rans_nnode)
vortvec = algs.vorticity(U)
for j in range(0, 3):
for i in range(0, 3):
for k in range(0, 3):
A += vortvec[:, j] * J[:, i, j] * vortvec[:, k] * J[:, i, k]
B = Snorm
# q[:,feat] = algs.sqrt(A)/(algs.sqrt(A)+B+small)
q[:, feat] = algs.sqrt(A) #/(algs.sqrt(A)+B+small)
feature_labels[feat] = 'Vortex stretching'
feat += 1
# Feature 8: Marker of Gorle et al. (deviation from parallel shear flow)
########################################################################
print('8: Marker of Gorle et al. (deviation from parallel shear flow)')
if nondim == 'global':
g = np.zeros([rans_nnode, 3])
m = np.zeros(rans_nnode)
s = U / Umag
for j in range(3):
for i in range(3):
g[:, j] += s[:, i] * J[:, i, j]
m += g[:, j] * s[:, j]
m = np.abs(m)
q[:, feat] = m * Ls / Us
elif nondim == 'local':
A = np.zeros(rans_nnode)
B = np.zeros(rans_nnode)
for i in range(0, 3):
for j in range(0, 3):
A += U[:, i] * U[:, j] * J[:, i, j]
for n in range(0, 3):
for i in range(0, 3):
for j in range(0, 3):
for m in range(0, 3):
B += U[:, n] * U[:, n] * U[:,
i] * J[:, i,
j] * U[:,
m] * J[:, m,
j]
q[:, feat] = algs.abs(A) / (algs.sqrt(B) + algs.abs(A) + small)
feature_labels[feat] = 'Deviation from parallel shear'
feat += 1
# Feature 9: Ratio of convection to production of k
####################################################
print('9: Ratio of convection to production of k')
uiuj = (2.0 / 3.0) * tke * delij - 2.0 * nu_t * Sij
dkdx = algs.gradient(tke)
A = np.zeros(rans_nnode)
B = np.zeros(rans_nnode)
for i in range(0, 3):
A += U[:, i] * dkdx[:, i]
for j in range(0, 3):
for l in range(0, 3):
B += uiuj[:, j, l] * Sij[:, j, l]
q[:, feat] = A / (algs.abs(B) + small)
feature_labels[feat] = 'Convection/production of k'
feat += 1
# Feature 10: Ratio of total Reynolds stresses to normal Reynolds stresses
##########################################################################
print('10: Ratio of total Reynolds stresses to normal Reynolds stresses')
# Frob. norm of uiuj
A = algs.sum(uiuj**2, axis=1) # sum i axis
A = algs.sum(A, axis=1) # sum previous summations i.e. along j axis
A = algs.sqrt(A)
B = tke
q[:, feat] = A / (B + small)
feature_labels[feat] = 'total/normal stresses'
feat += 1
# Feature 11: Cubic eddy viscosity comparision
##############################################
print('11: Cubic eddy viscosity comparision')
# Add quadratic and cubic terms to linear evm
cevm_2nd, cevm_3rd = build_cevm(Sij, Oij)
uiujSij = np.zeros(rans_nnode)
for i in range(0, 3):
for j in range(0, 3):
uiujSij += uiuj[:, i, j] * Sij[:, i, j]
uiujcevmSij = uiujSij + (cevm_2nd / tke) * nu_t**2.0 + (
cevm_3rd / tke**2.0) * nu_t**3.0
q[:, feat] = (uiujcevmSij -
uiujSij) / (0.5 *
(np.abs(uiujcevmSij) + np.abs(uiujSij)) + small)
feature_labels[feat] = 'CEV comparison'
feat += 1
# Feature 12: Streamline normal pressure gradient
#################################################
print('12: Stream-normal pressure gradient')
A = algs.cross(U, dpdx)
A = np.sqrt(A[:, 0]**2 + A[:, 1]**2 + A[:, 2]**2)
if nondim == 'global':
A = A / Umag
q[:, feat] = A * Ls / Ps
elif nondim == 'local':
B = np.zeros(rans_nnode)
for i in range(0, 3):
for j in range(0, 3):
B += U[:, i] * U[:, i] * dpdx[:, j] * dpdx[:, j]
q[:, feat] = A / (A + algs.sqrt(B) + small)
feature_labels[feat] = 'Stream-normal Pgrad'
feat += 1
# Feature 13: Streamline curvature
##################################
print('13: Streamline curvature')
# A = np.zeros([rans_nnode,3])
#
# # Gradient of Gamma
# Gamma = U#/algs.mag(U)
# dGammadx = algs.gradient(Gamma)
#
# for i in range(0,3):
# for j in range(0,3):
# A[:,i] += U[:,j]*dGammadx[:,j,i]
# A = algs.mag(A/algs.mag(U)*algs.mag(U))
#
# q[:,feat] = A
# feature_labels[feat] = 'Streamline curvature'
# feat += 1
D2 = 0.5 * (Snorm**2 + Onorm**2)
# cr1 = 1.0
# cr2 = 12.0
# cr3 = 1.0
cr2 = 12
cr3 = 1 / np.pi
rstar = Snorm / (Onorm + small)
dSijdx1 = algs.gradient(Sij[:, :, 0])
dSijdx2 = algs.gradient(Sij[:, :, 1])
dSijdx3 = algs.gradient(Sij[:, :, 2])
DSijDt = np.zeros([rans_nnode, 3, 3])
for i in range(3):
for j in range(3):
DSijDt[:, i,
j] = U[:,
0] * dSijdx1[:, j,
i] + U[:,
1] * dSijdx2[:, j,
i] + U[:,
2] * dSijdx3[:,
j,
i]
rhat = np.zeros(rans_nnode)
for i in range(3):
for j in range(3):
for k in range(3):
rhat += (2 * Oij[:, i, k] * Sij[:, j, k] /
D2**2) * DSijDt[:, i, j]
# fr1 = -((2*rstar)/(1+rstar))*(cr3*algs.arctan(cr2*rhat))
# fr1 = ( (1+cr1)*((2*rstar)/(1+rstar))*(1-cr3*algs.arctan(cr2*rhat)) ) - cr1
rhathat = algs.arctan(0.25 * rhat) * 2 / np.pi
q[:, feat] = rhathat #fr1
feature_labels[feat] = 'Streamline curvature'
feat += 1
# Feature 14: Anisotropy of pressure hessian
############################################
print('14: Anisotropy of pressure hessian')
# Calculate pressure hessian
Hij = algs.gradient(dpdx)
Hij = algs.apply_dfunc(np.transpose, Hij, (0, 2, 1))
aniso = np.zeros(rans_nnode)
iso = np.zeros(rans_nnode)
# Frob. norm of Hij
for i in range(3):
for j in range(3):
aniso += (Hij[:, i, j] - Hij[:, i, j] * delij[:, i, j])**2
iso += Hij[:, i, i]**2
aniso = np.sqrt(aniso)
iso = np.sqrt(iso)
q[:, feat] = (aniso) / (iso + small)
feature_labels[feat] = 'Anisotropy of pressure hessian'
feat += 1
# Feature 15: White noise
#########################
print('15: White noise')
q[:, feat] = np.random.uniform(low=-1.0, high=1.0, size=rans_nnode)
feature_labels[feat] = 'White noise'
feat += 1
return q, feature_labels