def calc_omega_residual(self, q):
nu = self.nu
sigma_w = self.sigma_w
beta_0 = self.beta_0
gamma_w = self.gamma_w
y = self.y
u, k, omega = get_var(q)
uy = diff(self.y, u)
ky = diff(self.y, k)
omegay = diff(self.y, omega)
omegayy = diff2(self.y, omega)
if self.model == None:
fac = self.beta
elif self.model == "linear":
fac = 1.0 + 2*y*y
#fac[0] = 1.0
elif self.model == "nn":
#print self.beta.shape
self.nn.set_from_vector(self.beta)
fac = 1.0 + self.nn.veval(self.y)
#fac[0] = 1.0
#print fac.shape
R = fac*gamma_w*uy**2 - beta_0*omega**2 + nu*omegayy + sigma_w*(omegay*(ky/omega - k*omegay/omega**2) + k/omega*omegayy)
R[0] = -(omega[0] - 5000000*nu/0.005**2)
R[-1] = 1/(y[-1] - y[-2])*(1.5*omega[-1] - 2.0*omega[-2] + 0.5*omega[-3])
return R
def calc_momentum_residual(self, q):
u, R11, R12, R22, R33, omega = get_var(q)
y = self.y
uy = diff(y, u)
uyy = diff2(y, u)
R = self.nu*uyy - self.dp/self.rho
R12y = diff(y, R12)
R = R + R12y
R[0] = -u[0]
R[-1] = (1.5*u[-1] - 2.0*u[-2] + 0.5*u[-3])/(y[-1] - y[-2])
return R
def calc_momentum_residual(self, q):
u, k, omega = get_var(q)
y = self.y
uy = diff(y, u)
uyy = diff2(y, u)
nut = k/(omega+1e-16)
nuty = diff(y, nut)
R = self.nu*uyy - self.dp/self.rho
R = R + nut*uyy + nuty*uy;
R[0] = -u[0]
R[-1] = (1.5*u[-1] - 2.0*u[-2] + 0.5*u[-3])/(y[-1] - y[-2])
return R
def calc_momentum_residual(self, q):
u, k, omega = get_var(q)
y = self.y
uy = diff(y, u)
uyy = diff2(y, u)
nut = k/(omega+1e-16)
nuty = diff(y, nut)
R = self.nu*uyy - self.dp/self.rho
R = R + nut*uyy + nuty*uy;
R[0] = -u[0]
R[-1] = (1.5*u[-1] - 2.0*u[-2] + 0.5*u[-3])/(y[-1] - y[-2])
return R
def calc_k_residual(self, q):
nu = self.nu
sigma_k = self.sigma_k
beta_s = self.beta_s
y = self.y
u, k, omega = get_var(q)
uy = diff(self.y, u)
ky = diff(self.y, k)
kyy = diff2(self.y, k)
omegay = diff(self.y, omega)
R = k/omega*uy**2 - beta_s*k*omega + nu*kyy + sigma_k*(kyy*k/omega + ky*(ky/omega - k/omega**2*omegay))
R[0] = -k[0]
R[-1] = 1/(y[-1] - y[-2])*(1.5*k[-1] - 2*k[-2] + 0.5*k[-3])
return R
def calc_k_residual(self, q):
nu = self.nu
sigma_k = self.sigma_k
beta_s = self.beta_s
y = self.y
u, k, omega = get_var(q)
uy = diff(self.y, u)
ky = diff(self.y, k)
kyy = diff2(self.y, k)
omegay = diff(self.y, omega)
R = k/omega*uy**2 - beta_s*k*omega + nu*kyy + sigma_k*(kyy*k/omega + ky*(ky/omega - k/omega**2*omegay))
R[0] = -k[0]
R[-1] = 1/(y[-1] - y[-2])*(1.5*k[-1] - 2*k[-2] + 0.5*k[-3])
return R
def calc_omega_residual(self, q):
nu = self.nu
sigma_w = self.sigma_w
beta_0 = self.beta_0
gamma_w = self.gamma_w
y = self.y
u, k, omega = get_var(q)
uy = diff(self.y, u)
ky = diff(self.y, k)
omegay = diff(self.y, omega)
omegayy = diff2(self.y, omega)
R = self.beta*gamma_w*uy**2 - beta_0*omega**2 + nu*omegayy + sigma_w*(omegay*(ky/omega - k*omegay/omega**2) + k/omega*omegayy)
R[0] = -(omega[0] - 5000000*nu/0.005**2)
R[-1] = 1/(y[-1] - y[-2])*(1.5*omega[-1] - 2.0*omega[-2] + 0.5*omega[-3])
return R
def calc_k_residual(self, q):
nu = self.nu
sigma_k = self.sigma_k
y = self.y
u, k, tau = get_var(q)
uy = diff(self.y, u)
ky = diff(self.y, k)
kyy = diff2(self.y, k)
tauy = diff(self.y, tau)
nut = self.calc_nut(q)
nuty = diff(self.y, nut)
R = nut*uy**2 - k/(tau + 1e-16) + kyy*(nu + nut/sigma_k) + nuty*ky/sigma_k
R[0] = -k[0]
R[-1] = 1/(y[-1] - y[-2])*(1.5*k[-1] - 2*k[-2] + 0.5*k[-3])
return R
def calc_R33_residual(self, q):
sigma_star = 0.6
nu = self.nu
y = self.y
u, R11, R12, R22, R33, omega = get_var(q)
R = np.zeros_like(u)
R33y = diff(y, R33)
R33yy = diff2(y, R33)
R = -self.P33 + self.eps_hat - self.PI33 + R33yy*(self.nu + self.nut*sigma_star) + sigma_star*self.nuty*R33y
R[0] = -R33[0]
R[-1] = (1.5*R33[-1] - 2.0*R33[-2] + 0.5*R33[-3])/(y[-1] - y[-2])
return R
def calc_R12_residual(self, q):
sigma_star = 0.6
nu = self.nu
y = self.y
u, R11, R12, R22, R33, omega = get_var(q)
R = np.zeros_like(u)
R12y = diff(y, R12)
R12yy = diff2(y, R12)
R = -self.P12 - self.PI12 + R12yy*(self.nu + self.nut*sigma_star) + sigma_star*self.nuty*R12y
R[0] = -R12[0]
R[-1] = -R12[-1]
return R
def calc_omega_residual(self, q):
nu = self.nu
alpha = 13.0/25.0
sigma = 0.5
beta_0 = 0.0708
y = self.y
u, R11, R12, R22, R33, omega = get_var(q)
k = self.k
nut = self.nut
nuty = diff(self.y, nut)
uy = diff(self.y, u)
ky = diff(self.y, k)
omegay = diff(self.y, omega)
omegayy = diff2(self.y, omega)
beta11, beta12, beta22, beta33 = get_beta(self.beta)
lastterm = 1/(omega + 1e-16)*ky*omegay
R = alpha*omega/(k + 1e-16)*R12*diff(y, u) - beta11*beta_0*omega**2 + omegayy*(nu + nut*sigma) + sigma*nuty*omegay + np.maximum(lastterm/8.0, 0.0)
R[0] = -(omega[0] - 5000000*nu/0.005**2)
R[-1] = 1/(y[-1] - y[-2])*(1.5*omega[-1] - 2.0*omega[-2] + 0.5*omega[-3])
return R
def calc_R22_residual(self, q):
sigma_star = 0.6
nu = self.nu
y = self.y
u, R11, R12, R22, R33, omega = get_var(q)
R = np.zeros_like(u)
R22y = diff(y, R22)
R22yy = diff2(y, R22)
beta11, beta12, beta22, beta33 = get_beta(self.beta)
R = -self.P22 + beta22*self.eps_hat - self.PI22 + R22yy*(self.nu + self.nut*sigma_star) + sigma_star*self.nuty*R22y
R[0] = -R22[0]
R[-1] = (1.5*R22[-1] - 2.0*R22[-2] + 0.5*R22[-3])/(y[-1] - y[-2])
return R
def load_solution_stressomega(dir_):
try:
y = np.loadtxt("%s/y" % dir_).astype(np.complex)
except:
y = np.loadtxt("base_solution/y").astype(np.complex)
u = np.loadtxt("%s/u" % dir_).astype(np.complex)
omega = np.loadtxt("%s/omega" % dir_).astype(np.complex)
k = np.loadtxt("%s/k" % dir_).astype(np.complex)
dudy = diff(y, u)
nut = k / (omega + 1e-16)
R11 = np.loadtxt("%s/R11" % dir_).astype(np.complex)[:201]
R12 = np.loadtxt("%s/R12" % dir_).astype(np.complex)[:201]
R22 = np.loadtxt("%s/R22" % dir_).astype(np.complex)[:201]
R33 = np.loadtxt("%s/R33" % dir_).astype(np.complex)[:201]
return y, u, R11, R12, R22, R33, omega
def calc_tau_residual(self, q):
nu = self.nu
sigma_tau2 = self.sigma_tau2
sigma_tau1 = self.sigma_tau1
Ceps1 = self.Ceps1
Ceps2 = self.Ceps2
A2 = self.A2
y = self.y
u, k, tau = get_var(q)
uy = diff(self.y, u)
ky = diff(self.y, k)
tauy = diff(self.y, tau)
tauyy = diff2(self.y, tau)
nut = self.calc_nut(q)
nuty = diff(self.y, nut)
utau = self.Retau*self.nu*2.0
yp = utau*self.y/self.nu
Ret = k*tau/self.nu
f2 = (1.0 - 2.0/9.0*np.exp(-Ret**2/36.0))*(1 - np.exp(-yp/A2))**2
R = (1.0 - Ceps1)*tau/(k+1e-16)*nut*uy**2 + (Ceps2*f2-1) + tauyy*(nu + nut/sigma_tau2) + tauy*nuty/sigma_tau2 - 2.0/(tau + 1e-16)*(nu + nut/sigma_tau2)*tauy**2 + 2.0/(k+1e-16)*(nu + nut/sigma_tau1)*ky*tauy
R[0] = -tau[0]
R[-1] = 1/(y[-1] - y[-2])*(1.5*tau[-1] - 2.0*tau[-2] + 0.5*tau[-3])
return R
def calc_prod_dest_pi(self, q):
C1 = 9./5.
C2 = 10./19.
alpha_h = (8. + C2)/11.
alpha = 13.0/25.0
sigma = 0.5
beta_0 = 0.0708
beta_star = 9.0/100.0
sigma_star = 0.6
sigma_do = 1.0/8.0
beta_0 = 0.0708
alpha_hat = (8.0 + C2)/11.0
beta_hat = (8.0*C2 - 2.0)/11.0
gamma_hat = (60.0*C2 - 4.0)/55.0
nu = self.nu
y = self.y
u, R11, R12, R22, R33, omega = get_var(q)
P11 = 2.0*R12*diff(y, u)
P12 = R22*diff(y, u)
P22 = np.zeros_like(u)
P33 = np.zeros_like(u)
D11 = np.zeros_like(u)
D12 = R11*diff(y, u)
D22 = 2.0*R12*diff(y, u)
D33 = np.zeros_like(u)
S11 = np.zeros_like(u)
S12 = 0.5*diff(y, u)
S22 = np.zeros_like(u)
S33 = np.zeros_like(u)
k = -0.5*(R11 + R22 + R33)
P_trace = 0.5*(P11 + P22 + P33)
S_trace = (S11 + S22 + S33)
PI11 = beta_star*C1*omega*(R11 + 2.0/3.0*k) - alpha_hat*(P11 - 2.0/3.0*P_trace) - beta_hat*(D11 - 2.0/3.0*P_trace)
PI12 = beta_star*C1*omega*R12 - alpha_hat*P12 - beta_hat*D12 - gamma_hat*k*S12
PI22 = beta_star*C1*omega*(R22 + 2.0/3.0*k) - alpha_hat*(P22 - 2.0/3.0*P_trace) - beta_hat*(D22 - 2.0/3.0*P_trace)
PI33 = beta_star*C1*omega*(R33 + 2.0/3.0*k) - alpha_hat*(P33 - 2.0/3.0*P_trace) - beta_hat*(D33 - 2.0/3.0*P_trace)
nut = k/(omega + 1e-16)
eps_hat = 2.0/3.0*beta_star*omega*k
self.P11 = P11
self.P12 = P12
self.P22 = P22
self.P33 = P33
self.D11 = D11
self.D12 = D12
self.D22 = D22
self.D33 = D33
self.PI11 = PI11
self.PI12 = PI12
self.PI22 = PI22
self.PI33 = PI33
self.eps_hat = eps_hat
# this might be dangerous
# global scoping of something
# that will be used as multiple places
self.k = k
self.nut = nut
self.nuty = diff(y, self.nut)