def setUp(self): PeriodicBox2DTestCaseCPU.setUp(self) self.orig_n = self.fluid.get_number_of_particles() self.nnps = LinkedListNNPS( dim=2, particles=[self.fluid], domain=self.domain, radius_scale=self.kernel.radius_scale)
def remove_overlap_particles(fluid_parray, solid_parray, dx_solid, dim=3): """ This function will take 2 particle arrays as input and will remove all the particles of the first particle array which are in the vicinity of the particles from second particle array. The function will remove all the particles within the dx_solid vicinity so some particles are removed at the outer surface of the particles from the second particle array. This uses a pysph nearest neighbour particles search which will output the particles within some range for every given particle. Parameters ---------- fluid_parray : a pysph particle array object solid_parray : a pysph particle array object dx_solid : a number which is the dx of the second particle array dim : dimensionality of the problem The particle arrays should atleast contain x, y and h values for a 2d case and atleast x, y, z and h values for a 3d case Returns ------- particle_array : pysph wcsph_particle_array with x, y, z and h values """ x = fluid_parray.x x1 = solid_parray.x y = fluid_parray.y y1 = solid_parray.y z = fluid_parray.z z1 = solid_parray.z h = fluid_parray.h if dim == 2: z = np.zeros_like(x) z1 = np.zeros_like(x1) modified_points = [] h_new = [] ll_nnps = LinkedListNNPS(dim, [fluid_parray, solid_parray]) for i in range(len(x)): nbrs = UIntArray() ll_nnps.get_nearest_particles(1, 0, i, nbrs) point_i = np.array([x[i], y[i], z[i]]) near_points = nbrs.get_npy_array() distances = [] for ind in near_points: dest = [x1[ind], y1[ind], z1[ind]] distances.append(distance(point_i, dest)) if len(distances) == 0: modified_points.append(point_i) h_new.append(h[i]) elif min(distances) >= (dx_solid * (1.0 - 1.0e-07)): modified_points.append(point_i) h_new.append(h[i]) modified_points = np.array(modified_points) x_new = modified_points[:, 0] y_new = modified_points[:, 1] z_new = modified_points[:, 2] p_array = get_particle_array_wcsph(x=x_new, y=y_new, z=z_new, h=h_new) return p_array
def setUp(self): TestPeriodicChannel3D.setUp(self) l = self.l self.domain = DomainManager(zmin=-l / 2.0, zmax=l / 2.0, periodic_in_z=True) self.nnps = LinkedListNNPS(dim=3, particles=self.particles, domain=self.domain, radius_scale=self.kernel.radius_scale)
def _setup_integrator(self, equations, integrator): kernel = CubicSpline(dim=1) arrays = [self.pa] a_eval = AccelerationEval(particle_arrays=arrays, equations=equations, kernel=kernel) comp = SPHCompiler(a_eval, integrator=integrator) comp.compile() nnps = LinkedListNNPS(dim=kernel.dim, particles=arrays) a_eval.set_nnps(nnps) integrator.set_nnps(nnps)
def find_overlap_particles(fluid_parray, solid_parray, dx_solid, dim=3): """This function will take 2 particle arrays as input and will find all the particles of the first particle array which are in the vicinity of the particles from second particle array. The function will find all the particles within the dx_solid vicinity so some particles may be identified at the outer surface of the particles from the second particle array. The particle arrays should atleast contain x, y and h values for a 2d case and atleast x, y, z and h values for a 3d case. Parameters ---------- fluid_parray : a pysph particle array object solid_parray : a pysph particle array object dx_solid : a number which is the dx of the second particle array dim : dimensionality of the problem Returns ------- list of particle indices to remove from the first array. """ x = fluid_parray.x x1 = solid_parray.x y = fluid_parray.y y1 = solid_parray.y z = fluid_parray.z z1 = solid_parray.z if dim == 2: z = np.zeros_like(x) z1 = np.zeros_like(x1) to_remove = [] ll_nnps = LinkedListNNPS(dim, [fluid_parray, solid_parray]) for i in range(len(x)): nbrs = UIntArray() ll_nnps.get_nearest_particles(1, 0, i, nbrs) point_i = np.array([x[i], y[i], z[i]]) near_points = nbrs.get_npy_array() distances = [] for ind in near_points: dest = [x1[ind], y1[ind], z1[ind]] distances.append(distance(point_i, dest)) if len(distances) == 0: continue elif min(distances) < (dx_solid * (1.0 - 1.0e-07)): to_remove.append(i) return to_remove
def setUp(self): # create the particle arrays L = 1.0 n = 5 dx = L / n hdx = 1.5 self.L = L self.vol = vol = dx * dx * dx # fluid particles xx, yy, zz = np.mgrid[dx / 2:L:dx, dx / 2:L:dx, dx / 2:L:dx] x = xx.ravel() y = yy.ravel() z = zz.ravel() # particle positions p = self._get_pressure(x, y, z) h = np.ones_like(x) * hdx * dx # smoothing lengths m = np.ones_like(x) * vol # mass V = np.zeros_like(x) # volumes fluid = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, m=m, V=V, p=p) # particles and domain self.fluid = fluid self.domain = DomainManager(xmin=0, xmax=L, ymin=0, ymax=L, zmin=0, zmax=L, periodic_in_x=True, periodic_in_y=True, periodic_in_z=True) self.kernel = get_compiled_kernel(Gaussian(dim=3)) self.orig_n = self.fluid.get_number_of_particles() self.nnps = LinkedListNNPS(dim=3, particles=[self.fluid], domain=self.domain, radius_scale=self.kernel.radius_scale)
def test_setting_use_cache_does_cache(self): # Given pa = self._make_random_parray('pa1', 3) pa.h[:] = 1.0 nnps = LinkedListNNPS(dim=3, particles=[pa], cache=False) n = pa.get_number_of_particles() # When nnps.set_use_cache(True) nbrs = UIntArray() nnps.set_context(0, 0) for i in range(n): nnps.get_nearest_particles(0, 0, i, nbrs) # Then self.assertEqual(nbrs.length, n) # Find the length of all cached neighbors, # in this case, each particle has n neighbors, # so we should have n*n neighbors in all. total_length = sum(x.length for x in nnps.cache[0]._neighbor_arrays) self.assertEqual(total_length, n * n)
def test_cache_updates_with_changed_particles(self): # Given pa1 = self._make_random_parray('pa1', 5) particles = [pa1] nnps = LinkedListNNPS(dim=3, particles=particles) cache = NeighborCache(nnps, dst_index=0, src_index=0) cache.update() # When pa2 = self._make_random_parray('pa2', 2) pa1.add_particles(x=pa2.x, y=pa2.y, z=pa2.z) nnps.update() cache.update() nb_cached = UIntArray() nb_direct = UIntArray() for i in range(len(particles[0].x)): nnps.get_nearest_particles_no_cache(0, 0, i, nb_direct, False) cache.get_neighbors(0, i, nb_cached) nb_e = nb_direct.get_npy_array() nb_c = nb_cached.get_npy_array() self.assertTrue(np.all(nb_e == nb_c))
def test_neighbors_cached_properly(self): # Given pa1 = self._make_random_parray('pa1', 5) pa2 = self._make_random_parray('pa2', 4) particles = [pa1, pa2] nnps = LinkedListNNPS(dim=3, particles=particles) for dst_index in (0, 1): for src_idx in (0, 1): # When cache = NeighborCache(nnps, dst_index, src_idx) cache.update() nb_cached = UIntArray() nb_direct = UIntArray() # Then. for i in range(len(particles[dst_index].x)): nnps.get_nearest_particles_no_cache( src_idx, dst_index, i, nb_direct, False) cache.get_neighbors(src_idx, i, nb_cached) nb_e = nb_direct.get_npy_array() nb_c = nb_cached.get_npy_array() self.assertTrue(np.all(nb_e == nb_c))
def test_empty_neigbors_works_correctly(self): # Given pa1 = self._make_random_parray('pa1', 5) pa2 = self._make_random_parray('pa2', 2) pa2.x += 10.0 particles = [pa1, pa2] # When nnps = LinkedListNNPS(dim=3, particles=particles) # Cache for neighbors of destination 0. cache = NeighborCache(nnps, dst_index=0, src_index=1) cache.update() # Then nb_cached = UIntArray() nb_direct = UIntArray() for i in range(len(particles[0].x)): nnps.get_nearest_particles_no_cache(1, 0, i, nb_direct, False) # Get neighbors from source 1 on destination 0. cache.get_neighbors(src_index=1, d_idx=i, nbrs=nb_cached) nb_e = nb_direct.get_npy_array() nb_c = nb_cached.get_npy_array() self.assertEqual(len(nb_e), 0) self.assertTrue(np.all(nb_e == nb_c))
def __init__(self, all_particles, scheme, domain=None, innerloop=True, updates=True, parallel=False, steps=None, D=0): """The second integrator is a simple Euler-Integrator (accurate enough due to very small time steps; very fast) using EBGSteps. EBGSteps are basically the same as EulerSteps, exept for the fact that they work with an intermediate ebg velocity [eu, ev, ew]. This velocity does not interfere with the actual velocity, which is neseccery to not disturb the real velocity through artificial damping in this step. The ebg velocity is initialized for each inner loop again and reset in the outer loop.""" from math import ceil from pysph.base.kernels import CubicSpline from pysph.sph.integrator_step import EBGStep from compyle.config import get_config from pysph.sph.integrator import EulerIntegrator from pysph.sph.scheme import BeadChainScheme from pysph.sph.equation import Group from pysph.sph.fiber.utils import (HoldPoints, Contact, ComputeDistance) from pysph.sph.fiber.beadchain import (Tension, Bending, ArtificialDamping) from pysph.base.nnps import DomainManager, LinkedListNNPS from pysph.sph.acceleration_eval import AccelerationEval from pysph.sph.sph_compiler import SPHCompiler if not isinstance(scheme, BeadChainScheme): raise TypeError("Scheme must be BeadChainScheme") self.innerloop = innerloop self.dt = scheme.dt self.fiber_dt = scheme.fiber_dt self.domain_updates = updates self.steps = steps self.D = D self.eta0 = scheme.rho0 * scheme.nu # if there are more than 1 particles involved, elastic equations are # iterated in an inner loop. if self.innerloop: # second integrator # self.fiber_integrator = EulerIntegrator(fiber=EBGStep()) steppers = {} for f in scheme.fibers: steppers[f] = EBGStep() self.fiber_integrator = EulerIntegrator(**steppers) # The type of spline has no influence here. It must be large enough # to contain the next particle though. kernel = CubicSpline(dim=scheme.dim) equations = [] g1 = [] for fiber in scheme.fibers: g1.append(ComputeDistance(dest=fiber, sources=[fiber])) equations.append(Group(equations=g1)) g2 = [] for fiber in scheme.fibers: g2.append( Tension(dest=fiber, sources=None, ea=scheme.E * scheme.A)) g2.append( Bending(dest=fiber, sources=None, ei=scheme.E * scheme.Ip)) g2.append( Contact(dest=fiber, sources=scheme.fibers, E=scheme.E, d=scheme.dx, dim=scheme.dim, k=scheme.k, lim=scheme.lim, eta0=self.eta0)) g2.append(ArtificialDamping(dest=fiber, sources=None, d=self.D)) equations.append(Group(equations=g2)) g3 = [] for fiber in scheme.fibers: g3.append(HoldPoints(dest=fiber, sources=None, tag=100)) equations.append(Group(equations=g3)) # These equations are applied to fiber particles only - that's the # reason for computational speed up. particles = [p for p in all_particles if p.name in scheme.fibers] # A seperate DomainManager is needed to ensure that particles don't # leave the domain. if domain: xmin = domain.manager.xmin ymin = domain.manager.ymin zmin = domain.manager.zmin xmax = domain.manager.xmax ymax = domain.manager.ymax zmax = domain.manager.zmax periodic_in_x = domain.manager.periodic_in_x periodic_in_y = domain.manager.periodic_in_y periodic_in_z = domain.manager.periodic_in_z gamma_yx = domain.manager.gamma_yx gamma_zx = domain.manager.gamma_zx gamma_zy = domain.manager.gamma_zy n_layers = domain.manager.n_layers N = self.steps or int(ceil(self.dt / self.fiber_dt)) # dt = self.dt/N self.domain = DomainManager(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, periodic_in_x=periodic_in_x, periodic_in_y=periodic_in_y, periodic_in_z=periodic_in_z, gamma_yx=gamma_yx, gamma_zx=gamma_zx, gamma_zy=gamma_zy, n_layers=n_layers, dt=self.dt, calls_per_step=N) else: self.domain = None # A seperate list for the nearest neighbourhood search is # benefitial since it is much smaller than the original one. nnps = LinkedListNNPS(dim=scheme.dim, particles=particles, radius_scale=kernel.radius_scale, domain=self.domain, fixed_h=False, cache=False, sort_gids=False) # The acceleration evaluator needs to be set up in order to compile # it together with the integrator. if parallel: self.acceleration_eval = AccelerationEval( particle_arrays=particles, equations=equations, kernel=kernel) else: self.acceleration_eval = AccelerationEval( particle_arrays=particles, equations=equations, kernel=kernel, mode='serial') # Compilation of the integrator not using openmp, because the # overhead is too large for those few fiber particles. comp = SPHCompiler(self.acceleration_eval, self.fiber_integrator) if parallel: comp.compile() else: config = get_config() config.use_openmp = False comp.compile() config.use_openmp = True self.acceleration_eval.set_nnps(nnps) # Connecting neighbourhood list to integrator. self.fiber_integrator.set_nnps(nnps)
def setUp(self): PeriodicChannel2DTestCase.setUp(self) self.nnps = LinkedListNNPS(dim=2, particles=self.particles, domain=self.domain, radius_scale=self.kernel.radius_scale)
# use the helper function get_particle_array to create a ParticleArray pa = utils.get_particle_array(x=x, y=y, h=h, m=m, wij=wij) # the simulation domain used to request periodicity domain = DomainManager(xmin=0., xmax=1., ymin=0., ymax=1., periodic_in_x=True, periodic_in_y=True) # NNPS object for nearest neighbor queries nps = LinkedListNNPS(dim=2, particles=[ pa, ], radius_scale=k.radius_scale, domain=domain) # container for neighbors nbrs = UIntArray() # arrays including ghosts x, y, h, m = pa.get('x', 'y', 'h', 'm', only_real_particles=False) # iterate over destination particles t1 = time() max_ngb = -1 for i in range(pa.num_real_particles): xi = x[i] yi = y[i]