def test_optimised_and_full_communication_equivalence(stencil_name):
target = ps.Target.CPU
stencil = LBStencil(stencil_name)
domain_size = (4, ) * stencil.D
dh = ps.create_data_handling(domain_size, periodicity=(True, ) * stencil.D,
parallel=False, default_target=target)
pdf = dh.add_array("pdf", values_per_cell=len(stencil), dtype=np.int64)
dh.fill("pdf", 0, ghost_layers=True)
pdf_tmp = dh.add_array("pdf_tmp", values_per_cell=len(stencil), dtype=np.int64)
dh.fill("pdf_tmp", 0, ghost_layers=True)
gl = dh.ghost_layers_of_field("pdf")
num = 0
for idx, x in np.ndenumerate(dh.cpu_arrays['pdf']):
dh.cpu_arrays['pdf'][idx] = num
dh.cpu_arrays['pdf_tmp'][idx] = num
num += 1
lbm_config = LBMConfig(stencil=stencil, kernel_type="stream_pull_only")
lbm_opt = LBMOptimisation(symbolic_field=pdf, symbolic_temporary_field=pdf_tmp)
config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=True)
ac = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
ast = ps.create_kernel(ac, config=config)
stream = ast.compile()
full_communication = dh.synchronization_function(pdf.name, target=dh.default_target, optimization={"openmp": True})
full_communication()
dh.run_kernel(stream)
dh.swap("pdf", "pdf_tmp")
pdf_full_communication = np.copy(dh.cpu_arrays['pdf'])
num = 0
for idx, x in np.ndenumerate(dh.cpu_arrays['pdf']):
dh.cpu_arrays['pdf'][idx] = num
dh.cpu_arrays['pdf_tmp'][idx] = num
num += 1
optimised_communication = LBMPeriodicityHandling(stencil=stencil, data_handling=dh, pdf_field_name=pdf.name,
streaming_pattern='pull')
optimised_communication()
dh.run_kernel(stream)
dh.swap("pdf", "pdf_tmp")
if stencil.D == 3:
for i in range(gl, domain_size[0]):
for j in range(gl, domain_size[1]):
for k in range(gl, domain_size[2]):
for f in range(len(stencil)):
assert dh.cpu_arrays['pdf'][i, j, k, f] == pdf_full_communication[i, j, k, f], print(f)
else:
for i in range(gl, domain_size[0]):
for j in range(gl, domain_size[1]):
for f in range(len(stencil)):
assert dh.cpu_arrays['pdf'][i, j, f] == pdf_full_communication[i, j, f]
def test_pack_info():
with ManualCodeGenerationContext() as ctx:
f = ps.fields("f(9): [3D]")
generate_pack_info_for_field(ctx, 'MyPackInfo1', f)
lb_assignments = create_lb_update_rule(
stencil='D3Q19', method='srt').main_assignments
generate_pack_info_from_kernel(ctx, 'MyPackInfo2', lb_assignments)
stencil_str = options['stencil']
q = int(stencil_str[stencil_str.find('Q') + 1:])
pdfs, velocity_field = ps.fields(
"pdfs({q}), velocity(3) : double[3D]".format(q=q), layout='fzyx')
options['optimization']['symbolic_field'] = pdfs
vp = [('int32_t', 'cudaBlockSize0'), ('int32_t', 'cudaBlockSize1')]
lb_method = create_lb_method(**options)
# Kernels
options_without_opt = options.copy()
del options_without_opt['optimization']
update_rules = {}
for name, accessor in accessors.items():
update_rule = create_lb_update_rule(lb_method=lb_method,
kernel_type=accessor,
**options)
update_rule = insert_fast_divisions(update_rule)
update_rule = insert_fast_sqrts(update_rule)
update_rules[name] = update_rule
generate_sweep(ctx,
'UniformGridGPU_AA_LbKernel' + name,
update_rule,
inner_outer_split=True,
target='gpu',
gpu_indexing_params=sweep_params,
varying_parameters=vp)
# getter & setter
setter_assignments = macroscopic_values_setter(
lb_method,
def poiseuille_channel(target, stencil_name):
# physical parameters
rho_0 = 1.2 # density
eta = 0.2 # kinematic viscosity
width = 41 # of box
actual_width = width - 2 # subtract boundary layer from box width
ext_force_density = 0.2 / actual_width ** 2 # scale by width to keep stable
# LB parameters
lb_stencil = LBStencil(stencil_name)
if lb_stencil.D == 2:
L = (4, width)
elif lb_stencil.D == 3:
L = (4, width, 4)
else:
raise Exception()
periodicity = [True, False] + [True] * (lb_stencil.D - 2)
omega = lbmpy.relaxationrates.relaxation_rate_from_lattice_viscosity(eta)
# ## Data structures
dh = ps.create_data_handling(L, periodicity=periodicity, default_target=target)
src = dh.add_array('src', values_per_cell=len(lb_stencil))
dst = dh.add_array_like('dst', 'src')
ρ = dh.add_array('rho', latex_name='\\rho', values_per_cell=1)
u = dh.add_array('u', values_per_cell=dh.dim)
# LB Setup
lbm_config = LBMConfig(stencil=lb_stencil, relaxation_rate=omega, method=Method.TRT,
compressible=True, force_model=ForceModel.GUO,
force=tuple([ext_force_density] + [0] * (lb_stencil.D - 1)),
kernel_type='collide_only')
lbm_opt = LBMOptimisation(symbolic_field=src)
collision = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'velocity': u})
config = ps.CreateKernelConfig(cpu_openmp=False, target=dh.default_target)
stream_kernel = ps.create_kernel(stream, config=config).compile()
collision_kernel = ps.create_kernel(collision, config=config).compile()
# Boundaries
lbbh = LatticeBoltzmannBoundaryHandling(collision.method, dh, src.name, target=dh.default_target)
# ## Set up the simulation
init = macroscopic_values_setter(collision.method, velocity=(0,) * dh.dim,
pdfs=src.center_vector, density=ρ.center)
init_kernel = ps.create_kernel(init, ghost_layers=0).compile()
noslip = NoSlip()
wall_thickness = 2
if lb_stencil.D == 2:
lbbh.set_boundary(noslip, ps.make_slice[:, :wall_thickness])
lbbh.set_boundary(noslip, ps.make_slice[:, -wall_thickness:])
elif lb_stencil.D == 3:
lbbh.set_boundary(noslip, ps.make_slice[:, :wall_thickness, :])
lbbh.set_boundary(noslip, ps.make_slice[:, -wall_thickness:, :])
else:
raise Exception()
for bh in lbbh, :
assert len(bh._boundary_object_to_boundary_info) == 1, "Restart kernel to clear boundaries"
def init():
dh.fill(ρ.name, rho_0)
dh.fill(u.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(u.name, 0)
dh.run_kernel(init_kernel)
# In[6]:
sync_pdfs = dh.synchronization_function([src.name])
# Time loop
def time_loop(steps):
dh.all_to_gpu()
i = -1
last_max_vel = -1
for i in range(steps):
dh.run_kernel(collision_kernel)
sync_pdfs()
lbbh()
dh.run_kernel(stream_kernel)
dh.swap(src.name, dst.name)
# Consider early termination
if i % 100 == 0:
if u.name in dh.gpu_arrays:
dh.to_cpu(u.name)
uu = dh.gather_array(u.name)
# average periodic directions
if lb_stencil.D == 3: # dont' swap order
uu = np.average(uu, axis=2)
uu = np.average(uu, axis=0)
max_vel = np.nanmax(uu)
if np.abs(max_vel / last_max_vel - 1) < 5E-6:
break
last_max_vel = max_vel
# cut off wall regions
uu = uu[wall_thickness:-wall_thickness]
# correct for f/2 term
uu -= np.array([ext_force_density / 2 / rho_0] + [0] * (lb_stencil.D - 1))
return uu
init()
# Simulation
profile = time_loop(5000)
# compare against analytical solution
# The profile is of shape (n,3). Force is in x-direction
y = np.arange(len(profile[:, 0]))
mid = (y[-1] - y[0]) / 2 # Mid point of channel
expected = poiseuille_flow((y - mid), actual_width, ext_force_density, rho_0 * eta)
np.testing.assert_allclose(profile[:, 0], expected, rtol=0.006)
# Test zero vel in other directions
np.testing.assert_allclose(profile[:, 1:], np.zeros_like(profile[:, 1:]), atol=1E-9)
"pdfs({q}), velocity(3) : double[3D]".format(q=q), layout='fzyx')
options['optimization']['symbolic_field'] = pdfs
vp = [
('double', 'omega_0'),
('double', 'omega_1'),
('double', 'omega_2'),
('double', 'omega_3'),
('double', 'omega_4'),
('double', 'omega_5'),
('double', 'omega_6'),
('int32_t', 'cudaBlockSize0'),
('int32_t', 'cudaBlockSize1'),
]
lb_method = create_lb_method(**options)
update_rule = create_lb_update_rule(lb_method=lb_method, **options)
if not noopt:
update_rule = insert_fast_divisions(update_rule)
update_rule = insert_fast_sqrts(update_rule)
# CPU lattice model - required for macroscopic value computation, VTK output etc.
options_without_opt = options.copy()
del options_without_opt['optimization']
generate_lattice_model(ctx,
'UniformGridGPU_LatticeModel',
lb_method,
update_rule_params=options_without_opt)
# gpu LB sweep & boundaries
generate_sweep(ctx,
from pystencils_walberla import CodeGeneration, generate_sweep
with CodeGeneration() as ctx:
# LB options
options = {
'method': 'srt',
'stencil': 'D3Q19',
'relaxation_rate': sp.Symbol("omega"),
'field_name': 'pdfs',
'compressible': False,
'temporary_field_name': 'pdfs_tmp',
'optimization': {'cse_global': True,
'cse_pdfs': True,
'gpu_indexing_params': {'block_size': (128, 1, 1)}}
}
lb_method = create_lb_method(**options)
update_rule = create_lb_update_rule(lb_method=lb_method, **options)
# CPU lattice model - required for macroscopic value computation, VTK output etc.
generate_lattice_model(ctx, 'UniformGridGPU_LatticeModel', lb_method)
# gpu LB sweep & boundaries
generate_sweep(ctx, 'UniformGridGPU_LbKernel', update_rule, field_swaps=[('pdfs', 'pdfs_tmp')],
inner_outer_split=True, target='gpu')
generate_boundary(ctx, 'UniformGridGPU_NoSlip', NoSlip(), lb_method, target='gpu')
generate_boundary(ctx, 'UniformGridGPU_UBB', UBB([0.05, 0, 0]), lb_method, target='gpu')
# communication
generate_pack_info_from_kernel(ctx, 'UniformGridGPU_PackInfo', update_rule, target='gpu')
def create_model(domain_size,
num_phases,
coeff_a,
coeff_epsilon,
gabd,
alpha=1,
penalty_factor=0.01,
simplex_projection=False):
def lapl(e):
return sum(Diff(Diff(e, i), i) for i in range(dh.dim))
def interfacial_chemical_potential(c):
result = []
n = len(c)
for i in range(n):
entry = 0
for k in range(n):
if i == k:
continue
eps = coeff_epsilon[(k, i)] if i < k else coeff_epsilon[(i, k)]
entry += alpha**2 * eps**2 * (c[k] * lapl(c[i]) -
c[i] * lapl(c[k]))
result.append(entry)
return -sp.Matrix(result)
def bulk(c):
result = 0
for i in range(num_phases):
for j in range(i):
result += (c[i]**2 * c[j]**2) / (4 * coeff_a[i, j])
for i in range(num_phases):
for j in range(i):
for k in range(j):
result += gabd * c[i] * c[j] * c[k]
return result
# -------------- Data ------------------
dh = create_data_handling(domain_size,
periodicity=(True, True),
default_ghost_layers=2)
c = dh.add_array("c", values_per_cell=num_phases)
rho = dh.add_array("rho", values_per_cell=1)
mu = dh.add_array("mu", values_per_cell=num_phases, latex_name="\\mu")
force = dh.add_array("F", values_per_cell=dh.dim)
u = dh.add_array("u", values_per_cell=dh.dim)
# Distribution functions for each order parameter
pdf_field = []
pdf_dst_field = []
for i in range(num_phases):
pdf_field_local = dh.add_array(f"pdf_ch_{i}",
values_per_cell=9) # 9 for D2Q9
pdf_dst_field_local = dh.add_array(f"pdfs_ch_{i}_dst",
values_per_cell=9)
pdf_field.append(pdf_field_local)
pdf_dst_field.append(pdf_dst_field_local)
# Distribution functions for the hydrodynamics
pdf_hydro_field = dh.add_array("pdfs", values_per_cell=9)
pdf_hydro_dst_field = dh.add_array("pdfs_dst", values_per_cell=9)
# ------------- Compute kernels --------
c_vec = c.center_vector
f_penalty = penalty_factor * (1 - sum(c_vec[i]
for i in range(num_phases)))**2
f_bulk = bulk(c_vec) + f_penalty
print(f_bulk)
mu_eq = chemical_potentials_from_free_energy(f_bulk,
order_parameters=c_vec)
mu_eq += interfacial_chemical_potential(c_vec)
mu_eq = [expand_diff_full(mu_i, functions=c) for mu_i in mu_eq]
mu_assignments = [
Assignment(mu(i), discretize_spatial(mu_i, dx=1, stencil='isotropic'))
for i, mu_i in enumerate(mu_eq)
]
mu_compute_kernel = create_kernel(mu_assignments).compile()
mu_discretize_substitutions = forth_order_isotropic_discretize(mu)
force_rhs = force_from_phi_and_mu(order_parameters=c_vec,
dim=dh.dim,
mu=mu.center_vector)
force_rhs = force_rhs.subs(mu_discretize_substitutions)
force_assignments = [
Assignment(force(i), force_rhs[i]) for i in range(dh.dim)
]
force_kernel = create_kernel(force_assignments).compile()
ch_collide_kernels = []
ch_methods = []
for i in range(num_phases):
ch_method = cahn_hilliard_lb_method(LBStencil(Stencil.D2Q9),
mu(i),
relaxation_rate=1.0,
gamma=1.0)
ch_methods.append(ch_method)
lbm_config = LBMConfig(lb_method=ch_method,
kernel_type='collide_only',
density_input=c(i),
velocity_input=u.center_vector,
compressible=True)
lbm_opt = LBMOptimisation(symbolic_field=pdf_field[i])
ch_update_rule = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
ch_assign = ch_update_rule.all_assignments
ch_kernel = create_kernel(ch_assign).compile()
ch_collide_kernels.append(ch_kernel)
ch_stream_kernels = []
for i in range(num_phases):
ch_method = ch_methods[i]
lbm_config = LBMConfig(lb_method=ch_method,
kernel_type='stream_pull_only',
temporary_field_name=pdf_dst_field[i].name)
lbm_opt = LBMOptimisation(symbolic_field=pdf_field[i])
ch_update_rule = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
ch_assign = ch_update_rule.all_assignments
ch_kernel = create_kernel(ch_assign).compile()
ch_stream_kernels.append(ch_kernel)
# Defining the initialisation kernels for the C-H pdfs
init_kernels = []
for i in range(num_phases):
ch_method = ch_methods[i]
init_assign = pdf_initialization_assignments(
lb_method=ch_method,
density=c_vec[i],
velocity=(0, 0),
pdfs=pdf_field[i].center_vector)
init_kernel = create_kernel(init_assign).compile()
init_kernels.append(init_kernel)
getter_kernels = []
for i in range(num_phases):
cqc = ch_methods[i].conserved_quantity_computation
output_assign = cqc.output_equations_from_pdfs(
pdf_field[i].center_vector, {'density': c(i)})
getter_kernel = create_kernel(output_assign).compile()
getter_kernels.append(getter_kernel)
lbm_config = LBMConfig(kernel_type='collide_only',
relaxation_rate=1.0,
force=force,
compressible=True)
lbm_opt = LBMOptimisation(symbolic_field=pdf_hydro_field)
collide_assign = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
collide_kernel = create_kernel(collide_assign).compile()
lbm_config = LBMConfig(kernel_type='stream_pull_only',
temporary_field_name=pdf_hydro_dst_field.name,
output={
"density": rho,
"velocity": u
})
lbm_opt = LBMOptimisation(symbolic_field=pdf_hydro_field)
stream_assign = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
stream_kernel = create_kernel(stream_assign).compile()
method_collide = collide_assign.method
init_hydro_assign = pdf_initialization_assignments(
lb_method=method_collide,
density=rho.center,
velocity=u.center_vector,
pdfs=pdf_hydro_field.center_vector)
init_hydro_kernel = create_kernel(init_hydro_assign).compile()
output_hydro_assign = cqc.output_equations_from_pdfs(
pdf_hydro_field.center_vector, {
'density': rho.center,
'velocity': u.center_vector
}).all_assignments
# Creating getter kernel to extract quantities
getter_hydro_kernel = create_kernel(
output_hydro_assign).compile() # getter kernel
# Setting values of arrays
dh.cpu_arrays[c.name].fill(0)
dh.cpu_arrays[u.name].fill(0)
dh.cpu_arrays[rho.name].fill(1)
dh.cpu_arrays[mu.name].fill(0)
dh.cpu_arrays[force.name].fill(0)
def init():
for k in init_kernels:
dh.run_kernel(k)
dh.run_kernel(init_hydro_kernel)
pdf_sync_fns = []
for i in range(num_phases):
sync_fn = dh.synchronization_function([pdf_field[i].name])
pdf_sync_fns.append(sync_fn)
hydro_sync_fn = dh.synchronization_function([pdf_hydro_field.name])
c_sync_fn = dh.synchronization_function([c.name])
mu_sync = dh.synchronization_function([mu.name])
def run(steps):
for t in range(steps):
# μ and P
c_sync_fn()
dh.run_kernel(mu_compute_kernel)
mu_sync()
dh.run_kernel(force_kernel)
# Hydrodynamic LB
dh.run_kernel(collide_kernel) # running collision kernel
hydro_sync_fn()
dh.run_kernel(stream_kernel) # running streaming kernel
dh.swap(pdf_hydro_field.name, pdf_hydro_dst_field.name)
dh.run_kernel(getter_hydro_kernel)
# Cahn-Hilliard LBs
for i in range(num_phases):
dh.run_kernel(ch_collide_kernels[i])
pdf_sync_fns[i]()
dh.run_kernel(ch_stream_kernels[i])
dh.swap(pdf_field[i].name, pdf_dst_field[i].name)
dh.run_kernel(getter_kernels[i])
if simplex_projection:
simplex_projection_2d(dh.cpu_arrays[c.name])
return dh.cpu_arrays[c.name][1:-1, 1:-1, :]
return dh, init, run
def test_diffusion():
"""
Runs the "Diffusion from Plate in Uniform Flow" benchmark as it is described
in [ch. 8.6.3, The Lattice Boltzmann Method, Krüger et al.].
dC/dy = 0
┌───────────────┐
│ → → → │
C = 0 │ → u → │ dC/dx = 0
│ → → → │
└───────────────┘
C = 1
The analytical solution is given by:
C(x,y) = 1 * erfc(y / sqrt(4Dx/u))
The hydrodynamic field is not simulated, instead a constant velocity is assumed.
"""
pytest.importorskip("pycuda")
# Parameters
domain_size = (1600, 160)
omega = 1.38
diffusion = (1 / omega - 0.5) / 3
velocity = 0.05
time_steps = 50000
stencil = LBStencil(Stencil.D2Q9)
target = ps.Target.GPU
# Data Handling
dh = ps.create_data_handling(domain_size=domain_size,
default_target=target)
vel_field = dh.add_array('vel_field', values_per_cell=stencil.D)
dh.fill('vel_field', velocity, 0, ghost_layers=True)
dh.fill('vel_field', 0.0, 1, ghost_layers=True)
con_field = dh.add_array('con_field', values_per_cell=1)
dh.fill('con_field', 0.0, ghost_layers=True)
pdfs = dh.add_array('pdfs', values_per_cell=stencil.Q)
dh.fill('pdfs', 0.0, ghost_layers=True)
pdfs_tmp = dh.add_array('pdfs_tmp', values_per_cell=stencil.Q)
dh.fill('pdfs_tmp', 0.0, ghost_layers=True)
# Lattice Boltzmann method
lbm_config = LBMConfig(stencil=stencil,
method=Method.MRT,
relaxation_rates=[1, 1.5, 1, 1.5, 1],
velocity_input=vel_field,
output={'density': con_field},
compressible=True,
weighted=True,
kernel_type='stream_pull_collide')
lbm_opt = LBMOptimisation(symbolic_field=pdfs,
symbolic_temporary_field=pdfs_tmp)
config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=True)
method = create_lb_method(lbm_config=lbm_config)
method.set_conserved_moments_relaxation_rate(omega)
lbm_config = replace(lbm_config, lb_method=method)
update_rule = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
kernel = ps.create_kernel(update_rule, config=config).compile()
# PDF initalization
init = pdf_initialization_assignments(method, con_field.center,
vel_field.center_vector,
pdfs.center_vector)
dh.run_kernel(ps.create_kernel(init).compile())
dh.all_to_gpu()
# Boundary Handling
bh = LatticeBoltzmannBoundaryHandling(update_rule.method,
dh,
'pdfs',
name="bh",
target=dh.default_target)
add_box_boundary(bh, boundary=NeumannByCopy())
bh.set_boundary(DiffusionDirichlet(0), slice_from_direction('W', dh.dim))
bh.set_boundary(DiffusionDirichlet(1), slice_from_direction('S', dh.dim))
# Timeloop
for i in range(time_steps):
bh()
dh.run_kernel(kernel)
dh.swap("pdfs", "pdfs_tmp")
dh.all_to_cpu()
# Verification
x = np.arange(1, domain_size[0], 1)
y = np.arange(0, domain_size[1], 1)
X, Y = np.meshgrid(x, y)
analytical = np.zeros(domain_size)
analytical[1:, :] = np.vectorize(math.erfc)(
Y / np.vectorize(math.sqrt)(4 * diffusion * X / velocity)).transpose()
simulated = dh.gather_array('con_field', ghost_layers=False)
residual = 0
for i in x:
for j in y:
residual += (simulated[i, j] - analytical[i, j])**2
residual = math.sqrt(residual / (domain_size[0] * domain_size[1]))
assert residual < 1e-2
def test_total_momentum(method_enum, force_model, omega):
# for the EDM force model this test case not work. However it is successfully used in test_entropic_model
# Any attempt to adapted the EDM force model so it fullfills the test case did result in a failure in the
# entropic test case. Note also that the test runs for MRT and EMD
if force_model == ForceModel.EDM:
pytest.skip()
L = (16, 16)
stencil = LBStencil(Stencil.D2Q9)
F = (2e-4, -3e-4)
dh = ps.create_data_handling(L,
periodicity=True,
default_target=Target.CPU)
src = dh.add_array('src', values_per_cell=stencil.Q)
dst = dh.add_array_like('dst', 'src')
ρ = dh.add_array('rho', values_per_cell=1)
u = dh.add_array('u', values_per_cell=stencil.D)
lbm_config = LBMConfig(method=method_enum,
stencil=stencil,
relaxation_rate=omega,
compressible=True,
force_model=force_model,
force=F,
streaming_pattern='pull')
lbm_opt = LBMOptimisation(symbolic_field=src)
collision = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
config = ps.CreateKernelConfig(cpu_openmp=True, target=dh.default_target)
collision_kernel = ps.create_kernel(collision, config=config).compile()
def init():
dh.fill(ρ.name, 1)
dh.fill(u.name, 0)
setter = macroscopic_values_setter(collision.method,
velocity=(0, ) * dh.dim,
pdfs=src,
density=ρ.center,
set_pre_collision_pdfs=True)
kernel = ps.create_kernel(setter).compile()
dh.run_kernel(kernel)
sync_pdfs = dh.synchronization_function([src.name])
getter = macroscopic_values_getter(collision.method,
ρ.center,
u.center_vector,
src,
use_pre_collision_pdfs=True)
getter_kernel = ps.create_kernel(getter).compile()
def time_loop(steps):
dh.all_to_gpu()
for _ in range(steps):
dh.run_kernel(collision_kernel)
dh.swap(src.name, dst.name)
sync_pdfs()
dh.all_to_cpu()
t = 20
init()
time_loop(t)
dh.run_kernel(getter_kernel)
total = np.sum(dh.gather_array(u.name), axis=(0, 1))
assert np.allclose(total / np.prod(L) / F / t, 1)
def test_shear_flow(target, stencil_name):
# Cuda
if target == ps.Target.GPU:
pytest.importorskip("pycuda")
# LB parameters
stencil = LBStencil(stencil_name)
if stencil.D == 2:
L = (4, width)
elif stencil.D == 3:
L = (4, width, 4)
else:
raise Exception()
periodicity = [True, False] + [True] * (stencil.D - 2)
omega = relaxation_rate_from_lattice_viscosity(eta)
# ## Data structures
dh = ps.create_data_handling(L, periodicity=periodicity, default_target=target)
src = dh.add_array('src', values_per_cell=stencil.Q)
dst = dh.add_array_like('dst', 'src')
ρ = dh.add_array('rho', latex_name='\\rho', values_per_cell=1)
u = dh.add_array('u', values_per_cell=stencil.D)
p = dh.add_array('p', values_per_cell=stencil.D**2)
# LB Setup
lbm_config = LBMConfig(stencil=stencil, relaxation_rate=omega, method=Method.TRT,
compressible=True, kernel_type='collide_only')
lbm_opt = LBMOptimisation(symbolic_field=src)
collision = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'velocity': u})
config = ps.CreateKernelConfig(cpu_openmp=False, target=dh.default_target)
stream_kernel = ps.create_kernel(stream, config=config).compile()
collision_kernel = ps.create_kernel(collision, config=config).compile()
# Boundaries
lbbh = LatticeBoltzmannBoundaryHandling(collision.method, dh, src.name, target=dh.default_target)
# Second moment test setup
cqc = collision.method.conserved_quantity_computation
getter_eqs = cqc.output_equations_from_pdfs(src.center_vector,
{'moment2': p})
kernel_compute_p = ps.create_kernel(getter_eqs, config=config).compile()
# ## Set up the simulation
init = macroscopic_values_setter(collision.method, velocity=(0,) * dh.dim,
pdfs=src.center_vector, density=ρ.center)
init_kernel = ps.create_kernel(init, ghost_layers=0).compile()
vel_vec = sp.Matrix([0.5 * shear_velocity] + [0] * (stencil.D - 1))
if stencil.D == 2:
lbbh.set_boundary(UBB(velocity=vel_vec), ps.make_slice[:, :wall_thickness])
lbbh.set_boundary(UBB(velocity=-vel_vec), ps.make_slice[:, -wall_thickness:])
elif stencil.D == 3:
lbbh.set_boundary(UBB(velocity=vel_vec), ps.make_slice[:, :wall_thickness, :])
lbbh.set_boundary(UBB(velocity=-vel_vec), ps.make_slice[:, -wall_thickness:, :])
else:
raise Exception()
for bh in lbbh, :
assert len(bh._boundary_object_to_boundary_info) == 2, "Restart kernel to clear boundaries"
def init():
dh.fill(ρ.name, rho_0)
dh.fill(u.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(u.name, 0)
dh.run_kernel(init_kernel)
sync_pdfs = dh.synchronization_function([src.name])
# Time loop
def time_loop(steps):
dh.all_to_gpu()
for i in range(steps):
dh.run_kernel(collision_kernel)
sync_pdfs()
lbbh()
dh.run_kernel(stream_kernel)
dh.run_kernel(kernel_compute_p)
dh.swap(src.name, dst.name)
if u.name in dh.gpu_arrays:
dh.to_cpu(u.name)
uu = dh.gather_array(u.name)
# average periodic directions
if stencil.D == 3: # dont' swap order
uu = np.average(uu, axis=2)
uu = np.average(uu, axis=0)
if p.name in dh.gpu_arrays:
dh.to_cpu(p.name)
pp = dh.gather_array(p.name)
# average periodic directions
if stencil.D == 3: # dont' swap order
pp = np.average(pp, axis=2)
pp = np.average(pp, axis=0)
# cut off wall regions
uu = uu[wall_thickness:-wall_thickness]
pp = pp[wall_thickness:-wall_thickness]
if stencil.D == 2:
pp = pp.reshape((len(pp), 2, 2))
if stencil.D == 3:
pp = pp.reshape((len(pp), 3, 3))
return uu, pp
init()
# Simulation
profile, pressure_profile = time_loop(t_max)
expected = shear_flow(x=(np.arange(0, actual_width) + .5),
t=t_max,
nu=eta / rho_0,
v=shear_velocity,
h=actual_width,
k_max=100)
if stencil.D == 2:
shear_direction = np.array((1, 0), dtype=float)
shear_plane_normal = np.array((0, 1), dtype=float)
if stencil.D == 3:
shear_direction = np.array((1, 0, 0), dtype=float)
shear_plane_normal = np.array((0, 1, 0), dtype=float)
shear_rate = shear_velocity / actual_width
dynamic_viscosity = eta * rho_0
correction_factor = eta / (eta - 1. / 6.)
p_expected = rho_0 * np.identity(dh.dim) / 3.0 + dynamic_viscosity * shear_rate / correction_factor * (
np.outer(shear_plane_normal, shear_direction) + np.transpose(np.outer(shear_plane_normal, shear_direction)))
# Sustract the tensorproduct of the velosity to get the pressure
pressure_profile[:, 0, 0] -= rho_0 * profile[:, 0]**2
np.testing.assert_allclose(profile[:, 0], expected[1:-1], atol=1E-9)
for i in range(actual_width - 2):
np.testing.assert_allclose(pressure_profile[i], p_expected, atol=1E-9, rtol=1E-3)
def test_lees_edwards():
domain_size = (64, 64)
omega = 1.0 # relaxation rate of first component
shear_velocity = 0.1 # shear velocity
shear_dir = 0 # direction of shear flow
shear_dir_normal = 1 # direction normal to shear plane, for interpolation
stencil = LBStencil(Stencil.D2Q9)
dh = ps.create_data_handling(domain_size,
periodicity=True,
default_target=ps.Target.CPU)
src = dh.add_array('src', values_per_cell=stencil.Q)
dh.fill('src', 1.0, ghost_layers=True)
dst = dh.add_array_like('dst', 'src')
dh.fill('dst', 0.0, ghost_layers=True)
force = dh.add_array('force', values_per_cell=stencil.D)
dh.fill('force', 0.0, ghost_layers=True)
rho = dh.add_array('rho', values_per_cell=1)
dh.fill('rho', 1.0, ghost_layers=True)
u = dh.add_array('u', values_per_cell=stencil.D)
dh.fill('u', 0.0, ghost_layers=True)
counters = [
LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(stencil.D)
]
points_up = sp.Symbol('points_up')
points_down = sp.Symbol('points_down')
u_p = sp.Piecewise(
(1,
sp.And(ps.data_types.type_all_numbers(counters[1] <= 1, 'int'),
points_down)),
(-1,
sp.And(
ps.data_types.type_all_numbers(counters[1] >= src.shape[1] - 2,
'int'), points_up)),
(0, True)) * shear_velocity
lbm_config = LBMConfig(stencil=stencil,
relaxation_rate=omega,
compressible=True,
velocity_input=u.center_vector +
sp.Matrix([u_p, 0]),
density_input=rho,
force_model=ForceModel.LUO,
force=force.center_vector,
kernel_type='collide_only')
lbm_opt = LBMOptimisation(symbolic_field=src)
collision = create_lb_update_rule(lbm_config=lbm_config,
lbm_optimisation=lbm_opt)
to_insert = [
s.lhs for s in collision.subexpressions
if collision.method.first_order_equilibrium_moment_symbols[shear_dir]
in s.free_symbols
]
for s in to_insert:
collision = collision.new_with_inserted_subexpression(s)
ma = []
for a, c in zip(collision.main_assignments, collision.method.stencil):
if c[shear_dir_normal] == -1:
b = (True, False)
elif c[shear_dir_normal] == 1:
b = (False, True)
else:
b = (False, False)
a = ps.Assignment(a.lhs, a.rhs.replace(points_down, b[0]))
a = ps.Assignment(a.lhs, a.rhs.replace(points_up, b[1]))
ma.append(a)
collision.main_assignments = ma
stream = create_stream_pull_with_output_kernel(collision.method, src, dst,
{
'density': rho,
'velocity': u
})
config = ps.CreateKernelConfig(target=dh.default_target)
stream_kernel = ps.create_kernel(stream, config=config).compile()
collision_kernel = ps.create_kernel(collision, config=config).compile()
init = macroscopic_values_setter(collision.method,
velocity=(0, 0),
pdfs=src.center_vector,
density=rho.center)
init_kernel = ps.create_kernel(init, ghost_layers=0).compile()
offset = [0.0]
sync_pdfs = dh.synchronization_function([src.name],
functor=partial(
get_le_boundary_functor,
shear_offset=offset))
dh.run_kernel(init_kernel)
time = 500
dh.all_to_gpu()
for i in range(time):
dh.run_kernel(collision_kernel)
sync_pdfs()
dh.run_kernel(stream_kernel)
dh.swap(src.name, dst.name)
offset[0] += shear_velocity
dh.all_to_cpu()
nu = lattice_viscosity_from_relaxation_rate(omega)
h = domain_size[0]
k_max = 100
analytical_solution = get_solution_navier_stokes(
np.linspace(0.5, h - 0.5, h), time, nu, shear_velocity, h, k_max)
np.testing.assert_array_almost_equal(analytical_solution,
dh.gather_array(u.name)[0, :, 0],
decimal=5)
dh.fill(rho.name, 1.0, ghost_layers=True)
dh.run_kernel(init_kernel)
dh.fill(u.name, 0.0, ghost_layers=True)
dh.fill('force', 0.0, ghost_layers=True)
dh.cpu_arrays[force.name][64 // 3, 1, :] = [1e-2, -1e-1]
offset[0] = 0
time = 20
dh.all_to_gpu()
for i in range(time):
dh.run_kernel(collision_kernel)
sync_pdfs()
dh.run_kernel(stream_kernel)
dh.swap(src.name, dst.name)
dh.all_to_cpu()
vel_unshifted = np.array(dh.gather_array(u.name)[:, -3:-1, :])
dh.fill(rho.name, 1.0, ghost_layers=True)
dh.run_kernel(init_kernel)
dh.fill(u.name, 0.0, ghost_layers=True)
dh.fill('force', 0.0, ghost_layers=True)
dh.cpu_arrays[force.name][64 // 3, 1, :] = [1e-2, -1e-1]
offset[0] = 10
time = 20
dh.all_to_gpu()
for i in range(time):
dh.run_kernel(collision_kernel)
sync_pdfs()
dh.run_kernel(stream_kernel)
dh.swap(src.name, dst.name)
dh.all_to_cpu()
vel_shifted = np.array(dh.gather_array(u.name)[:, -3:-1, :])
vel_rolled = np.roll(vel_shifted, -offset[0], axis=0)
np.testing.assert_array_almost_equal(vel_unshifted, vel_rolled)
