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 test_divergence(stencil, dtype_device):
dtype, device = dtype_device
lattice = Lattice(stencil, dtype=dtype, device=device)
flow = DecayingTurbulence(50, 1, 0.05, lattice=lattice, ic_energy=0.5)
collision = BGKCollision(lattice, tau=flow.units.relaxation_parameter_lu)
streaming = StandardStreaming(lattice)
simulation = Simulation(flow=flow,
lattice=lattice,
collision=collision,
streaming=streaming)
ekin = flow.units.convert_incompressible_energy_to_pu(
torch.sum(lattice.incompressible_energy(
simulation.f))) * flow.units.convert_length_to_pu(1.0)**lattice.D
u0 = flow.units.convert_velocity_to_pu(lattice.u(simulation.f)[0])
u1 = flow.units.convert_velocity_to_pu(lattice.u(simulation.f)[1])
dx = flow.units.convert_length_to_pu(1.0)
grad_u0 = torch_gradient(u0, dx=dx, order=6).cpu().numpy()
grad_u1 = torch_gradient(u1, dx=dx, order=6).cpu().numpy()
divergence = np.sum(grad_u0[0] + grad_u1[1])
if lattice.D == 3:
u2 = flow.units.convert_velocity_to_pu(lattice.u(simulation.f)[2])
grad_u2 = torch_gradient(u2, dx=dx, order=6).cpu().numpy()
divergence += np.sum(grad_u2[2])
assert (flow.ic_energy == pytest.approx(lattice.convert_to_numpy(ekin),
rel=1))
assert (0 == pytest.approx(divergence, abs=2e-3))
def test_force_guo(ForceType, device):
dtype = torch.double
lattice = Lattice(D2Q9, dtype=dtype, device=device)
flow = PoiseuilleFlow2D(resolution=10,
reynolds_number=1,
mach_number=0.02,
lattice=lattice,
initialize_with_zeros=True)
force = ForceType(lattice,
tau=flow.units.relaxation_parameter_lu,
acceleration=flow.units.convert_acceleration_to_lu(
flow.acceleration))
collision = BGKCollision(lattice,
tau=flow.units.relaxation_parameter_lu,
force=force)
streaming = StandardStreaming(lattice)
simulation = Simulation(flow=flow,
lattice=lattice,
collision=collision,
streaming=streaming)
simulation.step(500)
# compare with reference solution
u_sim = flow.units.convert_velocity_to_pu(
lattice.convert_to_numpy(lattice.u(simulation.f)))
_, u_ref = flow.analytic_solution(flow.grid)
fluidnodes = np.where(np.logical_not(flow.boundaries[0].mask.cpu()))
assert u_ref[0].max() == pytest.approx(u_sim[0].max(), rel=0.005)
assert u_ref[0][fluidnodes] == pytest.approx(u_sim[0][fluidnodes],
rel=None,
abs=0.005 * u_ref[0].max())
def test_grid_fine_to_coarse_3d():
lattice = Lattice(D3Q27, 'cpu', dtype=torch.double)
flow_f = TaylorGreenVortex3D(40, 1600, 0.15, lattice)
collision_f = BGKCollision(lattice,
tau=flow_f.units.relaxation_parameter_lu)
sim_f = Simulation(flow_f, lattice, collision_f, streaming=None)
flow_c = TaylorGreenVortex3D(20, 1600, 0.15, lattice)
collision_c = BGKCollision(lattice,
tau=flow_c.units.relaxation_parameter_lu)
sim_c = Simulation(flow_c, lattice, collision_c, streaming=None)
f_c = grid_fine_to_coarse(lattice, sim_f.f,
flow_f.units.relaxation_parameter_lu,
flow_c.units.relaxation_parameter_lu)
p_c_init, u_c_init = flow_c.initial_solution(flow_c.grid)
rho_c_init = flow_c.units.convert_pressure_pu_to_density_lu(p_c_init)
u_c_init = flow_c.units.convert_velocity_to_lu(u_c_init)
shear_c_init = lattice.shear_tensor(sim_c.f)
shear_c = lattice.shear_tensor(f_c)
assert np.isclose(lattice.u(f_c).cpu().numpy(), u_c_init).all()
assert np.isclose(lattice.rho(f_c).cpu().numpy(), rho_c_init).all()
assert torch.isclose(f_c, sim_c.f).all()
assert torch.isclose(shear_c_init, shear_c).all()
def test_equilibrium_pressure_outlet(dtype_device):
dtype, device = dtype_device
lattice = Lattice(D2Q9, dtype=dtype, device=device)
class MyObstacle(Obstacle2D):
@property
def boundaries(self, *args):
x, y = self.grid
return [
EquilibriumBoundaryPU(
np.abs(x) < 1e-6, self.units.lattice, self.units,
np.array([self.units.characteristic_velocity_pu, 0])),
EquilibriumOutletP(self.units.lattice, [0, -1]),
EquilibriumOutletP(self.units.lattice, [0, 1]),
EquilibriumOutletP(self.units.lattice, [1, 0]),
BounceBackBoundary(self.mask, self.units.lattice)
]
flow = MyObstacle(30,
30,
reynolds_number=10,
mach_number=0.1,
lattice=lattice,
char_length_lu=10)
mask = np.zeros_like(flow.grid[0], dtype=np.bool)
mask[10:20, 10:20] = 1
flow.mask = mask
simulation = Simulation(
flow, lattice,
RegularizedCollision(lattice, flow.units.relaxation_parameter_lu),
StandardStreaming(lattice))
simulation.step(20)
rho = lattice.rho(simulation.f)
u = lattice.u(simulation.f)
feq = lattice.equilibrium(torch.ones_like(rho), u)
p = flow.units.convert_density_lu_to_pressure_pu(rho)
zeros = torch.zeros_like(p[0, -1, :])
assert torch.allclose(zeros, p[:, -1, :], rtol=0, atol=1e-4)
assert torch.allclose(zeros, p[:, :, 0], rtol=0, atol=1e-4)
assert torch.allclose(zeros, p[:, :, -1], rtol=0, atol=1e-4)
assert torch.allclose(feq[:, -1, 1:-1], feq[:, -2, 1:-1])
collision=collision_coarse,
streaming=streaming)
simulation_p.flow.units.characteristic_velocity_pu = simulation.flow.units.characteristic_velocity_pu
f_reporter_r = FReporter_coarse(data_interval)
f_reporter_r.fs = []
f_reporter_r.ts = []
simulation_p.reporters.append(f_reporter_r)
simulation_p.reporters.append(enstrophy_r)
simulation_p.reporters.append(energy_r)
simulation_p.reporters.append(spectrum_r)
with torch.no_grad():
simulation_p.step(n_steps - nr_init * data_interval)
error_rho = (loss_function(lattice.rho(f_output.cuda()),
lattice.rho(simulation_p.f)).item())
error_u = (loss_function(lattice.u(f_output.cuda()),
lattice.u(simulation_p.f)).item())
error_energy = (loss_function(energy(f_output.cuda()),
energy(simulation_p.f)).item())
error_enstrophy = (loss_function(enstrophy(f_output.cuda()),
enstrophy(simulation_p.f)).item())
print(
f"{other_model.__name__:<20s} {np.mean(error_u):.4e} {np.mean(error_rho):.4e} {np.mean(error_energy):.4e} {np.mean(error_enstrophy):.4e}"
)
energy_data.append([[other_model.__name__], [energy_r.out]])
enstrophy_data.append([[other_model.__name__], [enstrophy_r.out]])
spectrum_data.append([[other_model.__name__],
[np.array(spectrum_r.out)[-1, 2:]]])
f_image.append([[other_model.__name__], [f_reporter_r.fs]])