Merge branch 'fvm_testcase' into 'master'

Fvm testcase with fluctuations and reactions

Closes #36

See merge request !284

pystencils_tests/test_fvm.py

@@ -3,6 +3,7 @@ import pystencils as ps
 import numpy as np
 import pytest
 from itertools import product
+from pystencils.rng import random_symbol
 
 
 def advection_diffusion(dim: int):
@@ -125,6 +126,297 @@ def test_advection_diffusion_3d(velocity):
     advection_diffusion.runners[3](velocity)
 
 
+def advection_diffusion_fluctuations(dim: int):
+    # parameters
+    if dim == 2:
+        L = (32, 32)
+        stencil_factor = np.sqrt(1 / (1 + np.sqrt(2)))
+    elif dim == 3:
+        L = (16, 16, 16)
+        stencil_factor = np.sqrt(1 / (1 + 2 * np.sqrt(2) + 4.0 / 3.0 * np.sqrt(3)))
+
+    dh = ps.create_data_handling(domain_size=L, periodicity=True, default_target=ps.Target.CPU)
+
+    n_field = dh.add_array('n', values_per_cell=1)
+    j_field = dh.add_array('j', values_per_cell=3 ** dim // 2, field_type=ps.FieldType.STAGGERED_FLUX)
+    velocity_field = dh.add_array('v', values_per_cell=dim)
+
+    D = 0.00666
+    time = 10000
+
+    def grad(f):
+        return sp.Matrix([ps.fd.diff(f, i) for i in range(dim)])
+
+    flux_eq = - D * grad(n_field)
+    fvm_eq = ps.fd.FVM1stOrder(n_field, flux=flux_eq)
+
+    vof_adv = ps.fd.VOF(j_field, velocity_field, n_field)
+
+    # merge calculation of advection and diffusion terms
+    flux = []
+    for adv, div in zip(vof_adv, fvm_eq.discrete_flux(j_field)):
+        assert adv.lhs == div.lhs
+        flux.append(ps.Assignment(adv.lhs, adv.rhs + div.rhs))
+    flux = ps.AssignmentCollection(flux)
+
+    rng_symbol_gen = random_symbol(flux.subexpressions, dim=dh.dim)
+    for i in range(len(flux.main_assignments)):
+        n = j_field.staggered_stencil[i]
+        assert flux.main_assignments[i].lhs == j_field.staggered_access(n)
+        
+        # calculate mean density
+        dens = (n_field.neighbor_vector(n) + n_field.center_vector)[0] / 2
+        # multyply by smoothed haviside function so that fluctuation will not get bigger that the density
+        dens *= sp.Max(0, sp.Min(1.0, n_field.neighbor_vector(n)[0]) * sp.Min(1.0, n_field.center_vector[0]))
+        
+        # lenght of the vector
+        length = sp.sqrt(len(j_field.staggered_stencil[i]))
+        
+        # amplitude of the random fluctuations
+        fluct = sp.sqrt(2 * dens * D) * sp.sqrt(1 / length) * stencil_factor
+        # add fluctuations
+        fluct *= 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
+        
+        flux.main_assignments[i] = ps.Assignment(flux.main_assignments[i].lhs, flux.main_assignments[i].rhs + fluct)
+    
+    # Add the folding to the flux, so that the random numbers persist through the ghostlayers.
+    fold = {ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i):
+            ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i) % L[i] for i in range(len(L))}
+    flux.subs(fold)
+
+    flux_kernel = ps.create_staggered_kernel(flux).compile()
+
+    pde_kernel = ps.create_kernel(fvm_eq.discrete_continuity(j_field)).compile()
+
+    sync_conc = dh.synchronization_function([n_field.name])
+
+    # analytical density distribution calculation
+    def P(rho, density_init):
+        res = []
+        for r in rho:
+            res.append(np.power(density_init, r) * np.exp(-density_init) / np.math.gamma(r + 1))
+        return np.array(res)
+
+    def run(density_init: float, velocity: np.ndarray, time: int):
+        dh.fill(n_field.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(j_field.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+
+        # set initial values for velocity and density
+        for i in range(dim):
+            dh.fill(velocity_field.name, velocity[i], i, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(n_field.name, density_init)
+
+        measurement_intervall = 10
+        warm_up = 1000
+        data = []
+
+        sync_conc()
+        for i in range(warm_up):
+            dh.run_kernel(flux_kernel, seed=42, time_step=i)
+            dh.run_kernel(pde_kernel)
+            sync_conc()
+
+        for i in range(time):
+            dh.run_kernel(flux_kernel, seed=42, time_step=i + warm_up)
+            dh.run_kernel(pde_kernel)
+            sync_conc()
+            if(i % measurement_intervall == 0):
+                data = np.append(data, dh.gather_array(n_field.name).ravel(), 0)
+
+        # test mass conservation
+        np.testing.assert_almost_equal(dh.gather_array(n_field.name).mean(), density_init)
+
+        n_bins = 50
+
+        density_value, bins = np.histogram(data, density=True, bins=n_bins)
+        bins_mean = bins[:-1] + (bins[1:] - bins[:-1]) / 2
+        analytical_value = P(bins_mean, density_init)
+        print(density_value - analytical_value)
+        np.testing.assert_allclose(density_value, analytical_value, atol=2e-3)
+
+    return lambda density_init, v: run(density_init, np.array(v), time)
+
+
+advection_diffusion_fluctuations.runners = {}
+
+
+@pytest.mark.parametrize("velocity", list(product([0, 0.00041], [0, -0.00031])))
+@pytest.mark.parametrize("density", [27.0, 56.5])
+@pytest.mark.longrun
+def test_advection_diffusion_fluctuation_2d(density, velocity):
+    if 2 not in advection_diffusion_fluctuations.runners:
+        advection_diffusion_fluctuations.runners[2] = advection_diffusion_fluctuations(2)
+    advection_diffusion_fluctuations.runners[2](density, velocity)
+
+
+@pytest.mark.parametrize("velocity", [(0.0, 0.0, 0.0), (0.00043, -0.00017, 0.00028)])
+@pytest.mark.parametrize("density", [27.0, 56.5])
+@pytest.mark.longrun
+def test_advection_diffusion_fluctuation_3d(density, velocity):
+    if 3 not in advection_diffusion_fluctuations.runners:
+        advection_diffusion_fluctuations.runners[3] = advection_diffusion_fluctuations(3)
+    advection_diffusion_fluctuations.runners[3](density, velocity)
+
+
+def diffusion_reaction(fluctuations: bool):
+    # parameters
+    L = (32, 32)
+    stencil_factor = np.sqrt(1 / (1 + np.sqrt(2)))
+
+    dh = ps.create_data_handling(domain_size=L, periodicity=True, default_target=ps.Target.CPU)
+
+    species = 2
+    n_fields = []
+    j_fields = []
+    r_flux_fields = []
+    for i in range(species):
+        n_fields.append(dh.add_array(f'n_{i}', values_per_cell=1))
+        j_fields.append(dh.add_array(f'j_{i}', values_per_cell=3 ** dh.dim // 2,
+                                     field_type=ps.FieldType.STAGGERED_FLUX))
+        r_flux_fields.append(dh.add_array(f'r_{i}', values_per_cell=1))
+    velocity_field = dh.add_array('v', values_per_cell=dh.dim)
+
+    D = 0.00666
+    time = 1000
+    r_order = [2.0, 0.0]
+    r_rate_const = 0.00001
+    r_coefs = [-2, 1]
+
+    def grad(f):
+        return sp.Matrix([ps.fd.diff(f, i) for i in range(dh.dim)])
+
+    flux_eq = - D * grad(n_fields[0])
+    fvm_eq = ps.fd.FVM1stOrder(n_fields[0], flux=flux_eq)
+    vof_adv = ps.fd.VOF(j_fields[0], velocity_field, n_fields[0])
+    continuity_assignments = fvm_eq.discrete_continuity(j_fields[0])
+    # merge calculation of advection and diffusion terms
+    flux = []
+    for adv, div in zip(vof_adv, fvm_eq.discrete_flux(j_fields[0])):
+        assert adv.lhs == div.lhs
+        flux.append(ps.Assignment(adv.lhs, adv.rhs + div.rhs))
+    flux = ps.AssignmentCollection(flux)
+
+    if(fluctuations):
+        rng_symbol_gen = random_symbol(flux.subexpressions, dim=dh.dim)
+        for i in range(len(flux.main_assignments)):
+            n = j_fields[0].staggered_stencil[i]
+            assert flux.main_assignments[i].lhs == j_fields[0].staggered_access(n)
+            
+            # calculate mean density
+            dens = (n_fields[0].neighbor_vector(n) + n_fields[0].center_vector)[0] / 2
+            # multyply by smoothed haviside function so that fluctuation will not get bigger that the density
+            dens *= sp.Max(0,
+                           sp.Min(1.0, n_fields[0].neighbor_vector(n)[0]) * sp.Min(1.0, n_fields[0].center_vector[0]))
+            
+            # lenght of the vector
+            length = sp.sqrt(len(j_fields[0].staggered_stencil[i]))
+            
+            # amplitude of the random fluctuations
+            fluct = sp.sqrt(2 * dens * D) * sp.sqrt(1 / length) * stencil_factor
+            # add fluctuations
+            fluct *= 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
+            
+            flux.main_assignments[i] = ps.Assignment(flux.main_assignments[i].lhs, flux.main_assignments[i].rhs + fluct)
+        
+        # Add the folding to the flux, so that the random numbers persist through the ghostlayers.
+        fold = {ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i):
+                ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i) % L[i] for i in range(len(L))}
+        flux.subs(fold)
+
+    r_flux = ps.AssignmentCollection([ps.Assignment(j_fields[i].center, 0) for i in range(species)])
+    reaction = r_rate_const
+    for i in range(species):
+        reaction *= sp.Pow(n_fields[i].center, r_order[i])
+    if(fluctuations):
+        rng_symbol_gen = random_symbol(r_flux.subexpressions, dim=dh.dim)
+        reaction_fluctuations = sp.sqrt(sp.Abs(reaction)) * 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
+        reaction_fluctuations *= sp.Min(1, sp.Abs(reaction**2))
+    else:
+        reaction_fluctuations = 0.0
+    for i in range(species):
+        r_flux.main_assignments[i] = ps.Assignment(
+            r_flux_fields[i].center, (reaction + reaction_fluctuations) * r_coefs[i])
+
+    continuity_assignments.append(ps.Assignment(n_fields[0].center, n_fields[0].center + r_flux_fields[0].center))
+
+    flux_kernel = ps.create_staggered_kernel(flux).compile()
+    reaction_kernel = ps.create_kernel(r_flux).compile()
+
+    pde_kernel = ps.create_kernel(continuity_assignments).compile()
+
+    sync_conc = dh.synchronization_function([n_fields[0].name, n_fields[1].name])
+
+    def f(t, r, n0, fac, fluctuations):
+        """Calculates the amount of product created after a certain time of a reaction with form xA -> B
+
+        Args:
+            t: Time of the reation
+            r: Reaction rate constant
+            n0: Initial density of the 
+            fac: Reaction order of A (this in most cases equals the stochometric coefficient x)
+            fluctuations: Boolian whether fluctuations were included during the reaction.
+        """
+        if fluctuations:
+            return 1 / fac * (n0 + n0 / (n0 - (n0 + 1) * np.exp(fac * r * t)))
+        return 1 / fac * (n0 - (1 / (fac * r * t + (1 / n0))))
+
+    def run(density_init: float, velocity: np.ndarray, time: int):
+        for i in range(species):
+            dh.fill(n_fields[i].name, np.nan, ghost_layers=True, inner_ghost_layers=True)
+            dh.fill(j_fields[i].name, 0.0, ghost_layers=True, inner_ghost_layers=True)
+            dh.fill(r_flux_fields[i].name, 0.0, ghost_layers=True, inner_ghost_layers=True)
+
+        # set initial values for velocity and density
+        for i in range(dh.dim):
+            dh.fill(velocity_field.name, velocity[i], i, ghost_layers=True, inner_ghost_layers=True)
+        dh.fill(n_fields[0].name, density_init)
+        dh.fill(n_fields[1].name, 0.0)
+
+        measurement_intervall = 10
+        data = []
+        
+        sync_conc()
+        for i in range(time):
+            if(i % measurement_intervall == 0):
+                data.append([i, dh.gather_array(n_fields[1].name).mean(), dh.gather_array(n_fields[0].name).mean()])
+            dh.run_kernel(reaction_kernel, seed=41, time_step=i)
+            for s_idx in range(species):
+                flux_kernel(n_0=dh.cpu_arrays[n_fields[s_idx].name],
+                            j_0=dh.cpu_arrays[j_fields[s_idx].name],
+                            v=dh.cpu_arrays[velocity_field.name], seed=42 + s_idx, time_step=i)
+                pde_kernel(n_0=dh.cpu_arrays[n_fields[s_idx].name],
+                           j_0=dh.cpu_arrays[j_fields[s_idx].name],
+                           r_0=dh.cpu_arrays[r_flux_fields[s_idx].name])
+            sync_conc()
+
+        data = np.array(data).transpose()
+        x = data[0]
+        analytical_value = f(x, r_rate_const, density_init, abs(r_coefs[0]), fluctuations)
+
+        # test mass conservation
+        np.testing.assert_almost_equal(
+            dh.gather_array(n_fields[0].name).mean() + 2 * dh.gather_array(n_fields[1].name).mean(), density_init)
+
+        r_tol = 2e-3
+        if fluctuations:
+            r_tol = 3e-2
+        np.testing.assert_allclose(data[1], analytical_value, rtol=r_tol)
+
+    return lambda density_init, v: run(density_init, np.array(v), time)
+
+
+advection_diffusion_fluctuations.runners = {}
+
+
+@pytest.mark.parametrize("velocity", list(product([0, 0.0041], [0, -0.0031])))
+@pytest.mark.parametrize("density", [27.0, 56.5])
+@pytest.mark.parametrize("fluctuations", [False, True])
+@pytest.mark.longrun
+def test_diffusion_reaction(density, velocity, fluctuations):
+    diffusion_reaction.runner = diffusion_reaction(fluctuations)
+    diffusion_reaction.runner(density, velocity)
+
+
 def VOF2(j: ps.field.Field, v: ps.field.Field, ρ: ps.field.Field, simplify=True):
     """Volume-of-fluid discretization of advection