def les_update(u_ab, nut_, nut_form, dt, CG1, tstep, DynamicSmagorinsky, Cs,
u_CG1, u_filtered, Lij, Mij, JLM, JMM, dim, tensdim, G_matr,
G_under, ll, dummy, uiuj_pairs, Sijmats, Sijcomps, Sijfcomps,
delta_CG1_sq, Qij, Nij, JNN, JQN, **NS_namespace):
# Check if Cs is to be computed, if not update nut_ and break
if tstep % DynamicSmagorinsky["Cs_comp_step"] != 0:
# Update nut_
nut_()
# Break function
return
# All velocity components must be interpolated to CG1 then filtered
for i in xrange(dim):
# Interpolate to CG1
ll.interpolate(u_CG1[i], u_ab[i])
# Filter
tophatfilter(unfiltered=u_CG1[i], filtered=u_filtered[i], **vars())
# Compute Lij from dynamic modules function
compute_Lij(u=u_CG1, uf=u_filtered, **vars())
# Compute Mij from dynamic modules function
alpha = 2.
magS = compute_Mij(alphaval=alpha, u_nf=u_CG1, u_f=u_filtered, **vars())
# Lagrange average Lij and Mij
lagrange_average(J1=JLM, J2=JMM, Aij=Lij, Bij=Mij, **vars())
# Now u needs to be filtered once more
for i in xrange(dim):
# Filter
tophatfilter(unfiltered=u_filtered[i],
filtered=u_filtered[i],
weight=1,
**vars())
# Compute Qij from dynamic modules function
compute_Qij(uf=u_filtered, **vars())
# Compute Nij from dynamic modules function
alpha = 4.
compute_Nij(alphaval=alpha, u_f=u_filtered, **vars())
# Lagrange average Qij and Nij
lagrange_average(J1=JQN, J2=JNN, Aij=Qij, Bij=Nij, **vars())
# UPDATE Cs**2 = (JLM*JMM)/beta, beta = JQN/JNN
beta = (JQN.vector().array() / JNN.vector().array()).clip(min=0.5)
Cs.vector().set_local((np.sqrt(
(JLM.vector().array() / JMM.vector().array()) / beta)))
Cs.vector().apply("insert")
tophatfilter(unfiltered=Cs, filtered=Cs, N=2, weight=1, **vars())
Cs.vector().set_local(Cs.vector().array().clip(max=0.3))
Cs.vector().apply("insert")
# Update nut_
nut_.vector().set_local(Cs.vector().array()**2 *
delta_CG1_sq.vector().array() * magS)
nut_.vector().apply("insert")
def les_update(u_ab, nut_, nut_form, dt, CG1, tstep,
DynamicSmagorinsky, Cs, u_CG1, u_filtered, Lij, Mij,
JLM, JMM, dim, tensdim, G_matr, G_under, ll,
dummy, uiuj_pairs, Sijmats, Sijcomps, Sijfcomps, delta_CG1_sq,
Qij, Nij, JNN, JQN, **NS_namespace):
# Check if Cs is to be computed, if not update nut_ and break
if tstep%DynamicSmagorinsky["Cs_comp_step"] != 0:
# Update nut_
nut_()
# Break function
return
# All velocity components must be interpolated to CG1 then filtered
for i in xrange(dim):
# Interpolate to CG1
ll.interpolate(u_CG1[i], u_ab[i])
# Filter
tophatfilter(unfiltered=u_CG1[i], filtered=u_filtered[i], **vars())
# Compute Lij from dynamic modules function
compute_Lij(u=u_CG1, uf=u_filtered, **vars())
# Compute Mij from dynamic modules function
alpha = 2.
magS = compute_Mij(alphaval=alpha, u_nf=u_CG1, u_f=u_filtered, **vars())
# Lagrange average Lij and Mij
lagrange_average(J1=JLM, J2=JMM, Aij=Lij, Bij=Mij, **vars())
# Now u needs to be filtered once more
for i in xrange(dim):
# Filter
tophatfilter(unfiltered=u_filtered[i], filtered=u_filtered[i],
weight=1, **vars())
# Compute Qij from dynamic modules function
compute_Qij(uf=u_filtered, **vars())
# Compute Nij from dynamic modules function
alpha = 4.
compute_Nij(alphaval=alpha, u_f=u_filtered, **vars())
# Lagrange average Qij and Nij
lagrange_average(J1=JQN, J2=JNN, Aij=Qij, Bij=Nij, **vars())
# UPDATE Cs**2 = (JLM*JMM)/beta, beta = JQN/JNN
beta = (JQN.vector().array()/JNN.vector().array()).clip(min=0.5)
Cs.vector().set_local((np.sqrt((JLM.vector().array()/JMM.vector().array())/beta)))
Cs.vector().apply("insert")
tophatfilter(unfiltered=Cs, filtered=Cs, N=2, weight=1, **vars())
Cs.vector().set_local(Cs.vector().array().clip(max=0.3))
Cs.vector().apply("insert")
# Update nut_
nut_.vector().set_local(Cs.vector().array()**2 * delta_CG1_sq.vector().array() * magS)
nut_.vector().apply("insert")
def les_update(u_ab, nut_, nut_form, dt, CG1, delta, tstep,
DynamicSmagorinsky, Cs, u_CG1, u_filtered, Lij, Mij,
JLM, JMM, dim, tensdim, G_matr, G_under, ll,
dummy, uiuj_pairs, Sijmats, Sijcomps, Sijfcomps, delta_CG1_sq,
**NS_namespace):
# Check if Cs is to be computed, if not update nut_ and break
if tstep%DynamicSmagorinsky["Cs_comp_step"] != 0:
# Update nut_
nut_()
# Break function
return
# All velocity components must be interpolated to CG1 then filtered
for i in xrange(dim):
# Interpolate to CG1
ll.interpolate(u_CG1[i], u_ab[i])
# Filter
tophatfilter(unfiltered=u_CG1[i], filtered=u_filtered[i], **vars())
# Compute Lij applying dynamic modules function
compute_Lij(u=u_CG1, uf=u_filtered, **vars())
# Compute Mij applying dynamic modules function
alpha = 2.0
magS = compute_Mij(alphaval=alpha, u_nf=u_CG1, u_f=u_filtered, **vars())
# Lagrange average Lij and Mij
lagrange_average(J1=JLM, J2=JMM, Aij=Lij, Bij=Mij, **vars())
# Update Cs = sqrt(JLM/JMM) and filter/smooth Cs, then clip at 0.3.
"""
Important that the term in nut_form is Cs**2 and not Cs
since Cs here is stored as sqrt(JLM/JMM).
"""
Cs.vector().set_local(np.sqrt(JLM.vector().array()/JMM.vector().array()))
Cs.vector().apply("insert")
tophatfilter(unfiltered=Cs, filtered=Cs, N=2, weight=1., **vars())
Cs.vector().set_local(Cs.vector().array().clip(max=0.3))
Cs.vector().apply("insert")
# Update nut_
nut_.vector().set_local(Cs.vector().array()**2 * delta_CG1_sq.vector().array() * magS)
nut_.vector().apply("insert")
def les_update(u_ab, nut_, nut_form, dt, CG1, delta, tstep, DynamicSmagorinsky,
Cs, u_CG1, u_filtered, Lij, Mij, JLM, JMM, dim, tensdim, G_matr,
G_under, ll, dummy, uiuj_pairs, Sijmats, Sijcomps, Sijfcomps,
delta_CG1_sq, **NS_namespace):
# Check if Cs is to be computed, if not update nut_ and break
if tstep % DynamicSmagorinsky["Cs_comp_step"] != 0:
# Update nut_
nut_()
# Break function
return
# All velocity components must be interpolated to CG1 then filtered
for i in xrange(dim):
# Interpolate to CG1
ll.interpolate(u_CG1[i], u_ab[i])
# Filter
tophatfilter(unfiltered=u_CG1[i], filtered=u_filtered[i], **vars())
# Compute Lij applying dynamic modules function
compute_Lij(u=u_CG1, uf=u_filtered, **vars())
# Compute Mij applying dynamic modules function
alpha = 2.0
magS = compute_Mij(alphaval=alpha, u_nf=u_CG1, u_f=u_filtered, **vars())
# Lagrange average Lij and Mij
lagrange_average(J1=JLM, J2=JMM, Aij=Lij, Bij=Mij, **vars())
# Update Cs = sqrt(JLM/JMM) and filter/smooth Cs, then clip at 0.3.
"""
Important that the term in nut_form is Cs**2 and not Cs
since Cs here is stored as sqrt(JLM/JMM).
"""
Cs.vector().set_local(np.sqrt(JLM.vector().array() / JMM.vector().array()))
Cs.vector().apply("insert")
tophatfilter(unfiltered=Cs, filtered=Cs, N=2, weight=1., **vars())
Cs.vector().set_local(Cs.vector().array().clip(max=0.3))
Cs.vector().apply("insert")
# Update nut_
nut_.vector().set_local(Cs.vector().array()**2 *
delta_CG1_sq.vector().array() * magS)
nut_.vector().apply("insert")
