def godunov(rho, u, p, dt, dx, gamma=1.4):
# Boundary values are the same.
rho_new = rho.copy()
rho_new[1:-1] = 0.0
u_new = u.copy()
u_new[1:-1] = 0.0
p_new = p.copy()
p_new[1:-1] = 0.0
for i in range(1, len(rho) - 1):
# Left flux
W_i = np.array([rho[i], u[i], p[i]])
W_l = np.array([rho[i - 1], u[i - 1], p[i - 1]])
W_r = W_i
riemann = Riemann(W_l, W_r, gamma, x0=0)
W = riemann.evaluate_single_state(0, 1) # dx/dt = 0
F_left = W2F(W, gamma=gamma)
# Right flux
W_l = W_i
W_r = np.array([rho[i + 1], u[i + 1], p[i + 1]])
riemann = Riemann(W_l, W_r, gamma, x0=0)
W = riemann.evaluate_single_state(0, 1)
F_right = W2F(W, gamma=gamma)
# Compute at next time step
U_i = W2U(W_i, gamma=gamma)
U_new = U_i - dt / dx * (F_right - F_left)
rho_new[i], u_new[i], p_new[i] = U2W(U_new)
return rho_new, u_new, p_new
def godunov(rho, u, p, dt, dx, gamma=1.4):
# Boundary values are the same.
rho_new = rho.copy()
rho_new[1:-1] = 0.0
u_new = u.copy()
u_new[1:-1] = 0.0
p_new = p.copy()
p_new[1:-1] = 0.0
for i in range(1, len(rho) - 1):
# Left flux
W_i = np.array([rho[i], u[i], p[i]])
W_l = np.array([rho[i - 1], u[i - 1], p[i - 1]])
W_r = W_i
riemann = Riemann(W_l, W_r, gamma, x0=0)
W = riemann.evaluate_single_state(0, 1) # dx/dt = 0
F_left = W2F(W, gamma=gamma)
# Right flux
W_l = W_i
W_r = np.array([rho[i + 1], u[i + 1], p[i + 1]])
riemann = Riemann(W_l, W_r, gamma, x0=0)
W = riemann.evaluate_single_state(0, 1)
F_right = W2F(W, gamma=gamma)
# Compute at next time step
U_i = W2U(W_i, gamma=gamma)
U_new = U_i - dt/dx*(F_right - F_left)
rho_new[i], u_new[i], p_new[i] = U2W(U_new)
return rho_new, u_new, p_new
def MUSCL(rho, u, p, dt, dx, gamma=1.4, limiter='zero'):
# Boundary values are the same.
rho_new = rho.copy()
rho_new[1:-1] = 0.0
u_new = u.copy()
u_new[1:-1] = 0.0
p_new = p.copy()
p_new[1:-1] = 0.0
# Update fluxes and conservative
W = np.empty((len(rho), 3))
U = np.empty_like(W)
for i in range(len(U)):
W[i] = np.array([rho[i], u[i], p[i]])
U[i] = W2U(W[i], gamma=gamma)
##################
# Predictor Step #
##################
# Compute limited slope
delta_W = limiters.limiter(W, name=limiter)
F_l = np.empty_like(delta_W)
F_r = F_l.copy()
U_tilde = F_l.copy()
W_tilde = F_l.copy()
for i in range(len(delta_W)):
F_l[i] = W2F(W[i] + 0.5 * delta_W[i])
F_r[i] = W2F(W[i] - 0.5 * delta_W[i])
U_tilde[i] = U[i] - dt / dx * (F_l[i] - F_r[i])
W_tilde[i] = U2W(U_tilde[i])
####################
## Corrector Step ##
####################
# Godunov-like
W_l = 0.5 * (W + W_tilde + delta_W)
W_r = 0.5 * (W + W_tilde - delta_W)
W_rs = np.zeros_like(W_l)
F_rs = W_rs.copy()
for i in range(0, len(W_l) - 1):
# Left flux
riemann = Riemann(W_l[i], W_r[i + 1], gamma, x0=0)
W_rs[i] = riemann.evaluate_single_state(0, 1)
F_rs[i] = W2F(W_rs[i])
U_new = U.copy()
U_new[1:-1] = U[1:-1] - dt / dx * (F_rs[1:-1] - F_rs[0:-2])
for i in range(len(U_new)):
rho_new[i], u_new[i], p_new[i] = U2W(U_new[i])
# pdb.set_trace()
return rho_new, u_new, p_new
def MUSCL(rho, u, p, dt, dx, gamma=1.4, limiter='zero'):
# Boundary values are the same.
rho_new = rho.copy()
rho_new[1:-1] = 0.0
u_new = u.copy()
u_new[1:-1] = 0.0
p_new = p.copy()
p_new[1:-1] = 0.0
# Update fluxes and conservative
W = np.empty((len(rho), 3))
U = np.empty_like(W)
for i in range(len(U)):
W[i] = np.array([rho[i], u[i], p[i]])
U[i] = W2U(W[i], gamma=gamma)
##################
# Predictor Step #
##################
# Compute limited slope
delta_W = limiters.limiter(W, name=limiter)
F_l = np.empty_like(delta_W)
F_r = F_l.copy()
U_tilde = F_l.copy()
W_tilde = F_l.copy()
for i in range(len(delta_W)):
F_l[i] = W2F(W[i] + 0.5*delta_W[i])
F_r[i] = W2F(W[i] - 0.5*delta_W[i])
U_tilde[i] = U[i] - dt/dx*(F_l[i] - F_r[i])
W_tilde[i] = U2W(U_tilde[i])
####################
## Corrector Step ##
####################
# Godunov-like
W_l = 0.5*(W + W_tilde + delta_W)
W_r = 0.5*(W + W_tilde - delta_W)
W_rs = np.zeros_like(W_l)
F_rs = W_rs.copy()
for i in range(0, len(W_l) - 1):
# Left flux
riemann = Riemann(W_l[i], W_r[i+1], gamma, x0=0)
W_rs[i] = riemann.evaluate_single_state(0, 1)
F_rs[i] = W2F(W_rs[i])
U_new = U.copy()
U_new[1:-1] = U[1:-1] - dt/dx*(F_rs[1:-1] - F_rs[0:-2])
for i in range(len(U_new)):
rho_new[i], u_new[i], p_new[i] = U2W(U_new[i])
# pdb.set_trace()
return rho_new, u_new, p_new
def _test_sample_singleeval(test_file, W_l, W_r, gamma, t, x0=0):
target_results = np.genfromtxt(test_file, skip_header=1)
riemann = Riemann(W_l, W_r, gamma, x0)
for x, rho, u, p in target_results:
W_actual = riemann.evaluate_single_state(x, t)
W_target = np.array([rho, u, p])
try:
compare(W_actual, W_target, 0.002)
except AssertionError:
print("x:", x)
print("t:", t)
print("S:", x / t)
raise
def _test_sample_singleeval(test_file, W_l, W_r, gamma, t, x0=0):
target_results = np.genfromtxt(test_file, skip_header=1)
riemann = Riemann(W_l, W_r, gamma, x0)
for x, rho, u, p in target_results:
W_actual = riemann.evaluate_single_state(x, t)
W_target = np.array([rho, u, p])
try:
compare(W_actual, W_target, 0.002)
except AssertionError:
print("x:", x)
print("t:", t)
print("S:", x / t)
raise
