def test_initialize_fneq(Case, dtype_device):
dtype, device = dtype_device
lattice = Lattice(D2Q9, device, dtype)
if Case == TaylorGreenVortex3D:
lattice = Lattice(D3Q27, dtype=dtype, device=device)
flow = Case(resolution=16, reynolds_number=1000, mach_number=0.01, lattice=lattice)
collision = BGKCollision(lattice, tau=flow.units.relaxation_parameter_lu)
streaming = StandardStreaming(lattice)
simulation_neq = Simulation(flow=flow, lattice=lattice, collision=collision, streaming=streaming)
pre_rho = lattice.rho(simulation_neq.f)
pre_u = lattice.u(simulation_neq.f)
simulation_neq.initialize_f_neq()
post_rho = lattice.rho(simulation_neq.f)
post_u = lattice.u(simulation_neq.f)
assert(torch.allclose(pre_rho,post_rho,1e-6))
assert(torch.allclose(pre_u,post_u))
if Case == TaylorGreenVortex2D:
error_reporter_neq = ErrorReporter(lattice, flow, interval=1, out=None)
error_reporter_eq = ErrorReporter(lattice, flow, interval=1, out=None)
simulation_eq = Simulation(flow=flow, lattice=lattice, collision=collision, streaming=streaming)
simulation_neq.reporters.append(error_reporter_neq)
simulation_eq.reporters.append(error_reporter_eq)
simulation_neq.step(10)
simulation_eq.step(10)
error_u, error_p = np.mean(np.abs(error_reporter_neq.out), axis=0).tolist()
error_u_eq, error_p_eq = np.mean(np.abs(error_reporter_eq.out), axis=0).tolist()
assert(error_u < error_u_eq)
def convergence(ctx, init_f_neq):
"""Use Taylor Green 2D for convergence test in diffusive scaling."""
device, dtype = ctx.obj['device'], ctx.obj['dtype']
lattice = Lattice(D2Q9, device, dtype)
error_u_old = None
error_p_old = None
print(("{:>15} " * 5).format("resolution", "error (u)", "order (u)",
"error (p)", "order (p)"))
for i in range(4, 9):
resolution = 2**i
mach_number = 8 / resolution
# Simulation
flow = TaylorGreenVortex2D(resolution=resolution,
reynolds_number=10000,
mach_number=mach_number,
lattice=lattice)
collision = BGKCollision(lattice,
tau=flow.units.relaxation_parameter_lu)
streaming = StandardStreaming(lattice)
simulation = Simulation(flow=flow,
lattice=lattice,
collision=collision,
streaming=streaming)
if (init_f_neq):
simulation.initialize_f_neq()
error_reporter = ErrorReporter(lattice, flow, interval=1, out=None)
simulation.reporters.append(error_reporter)
for _ in range(10 * resolution):
simulation.step(1)
error_u, error_p = np.mean(np.abs(error_reporter.out), axis=0).tolist()
factor_u = 0 if error_u_old is None else error_u_old / error_u
factor_p = 0 if error_p_old is None else error_p_old / error_p
error_u_old = error_u
error_p_old = error_p
print("{:15} {:15.2e} {:15.1f} {:15.2e} {:15.1f}".format(
resolution, error_u, factor_u / 2, error_p, factor_p / 2))
if factor_u / 2 < 1.9:
print("Velocity convergence order < 2.")
if factor_p / 2 < 0.9:
print("Velocity convergence order < 1.")
if factor_u / 2 < 1.9 or factor_p / 2 < 0.9:
sys.exit(1)
else:
return 0
interval=postplotinterval * 2,
out=None)
energy_f = ObservableReporter(IncompressibleKineticEnergy(lattice, flow_fine),
interval=postplotinterval * 2,
out=None)
spectrum_f = ObservableReporter(EnergySpectrum(lattice, flow_fine),
interval=postplotinterval * 2,
out=None)
f_reporter = FReporter(2 * data_interval)
simulation.reporters.append(f_reporter)
simulation.reporters.append(enstrophy_f)
simulation.reporters.append(energy_f)
simulation.reporters.append(spectrum_f)
simulation.initialize_pressure()
simulation.initialize_f_neq()
_ = simulation.step(2 * n_steps)
########################################################################################################################
##### Make Data Pairs
########################################################################################################################
print('Make Data Pairs and shuffle training data')
data = torch.stack(f_reporter.fs).cpu()
f_reporter.fs = []
inputs = data[0:-1]
outputs = data[1:]
f_init = inputs[nr_init].clone()
f_output = outputs[-1].clone()
