def test_should_pass_when_src_props_exist(self):
# Given
f = get_particle_array(name='f')
f.add_property('V')
s = get_particle_array(name='s')
s.add_property('V')
# When
eq = TestEquation(dest='f', sources=['f', 's'])
# Then
check_equation_array_properties(eq, [f, s])
def test_should_fail_when_src_props_dont_exist(self):
# Given
f = get_particle_array(name='f')
f.add_property('V')
s = get_particle_array(name='s')
# When
eq = TestEquation(dest='f', sources=['f', 's'])
# Then
self.assertRaises(RuntimeError,
check_equation_array_properties, eq, [f, s])
def setUp(self):
self.dim = 1
n = 10
dx = 1.0/(n-1)
x = np.linspace(0, 1, n)
m = np.ones_like(x)
h = np.ones_like(x)*dx
pa = get_particle_array(name='fluid', x=x, h=h, m=m)
self.pa = pa
def test_should_pass_when_properties_exist(self):
# Given
f = get_particle_array(name='f')
# When
eq = SummationDensity(dest='f', sources=['f'])
# Then
check_equation_array_properties(eq, [f])
def bench_uniform_distribution():
arrays = []
for numPoints in _numPoints:
nx = numpy.ceil(numpy.power(numPoints, 1./3))
dx = 1./nx
xa, ya, za = numpy.mgrid[0:1:dx, 0:1:dx, 0:1:dx]
xa = xa.ravel(); ya = ya.ravel(); za = za.ravel()
ha = numpy.ones_like(xa) * 2.0*dx
gida = numpy.arange(xa.size).astype(numpy.uint32)
# create the particle array
pa = get_particle_array(x=xa, y=ya, z=za, h=ha, gid=gida)
# create the particle array
pa = get_particle_array(x=xa, y=ya, z=za, h=ha, gid=gida)
arrays.append(pa)
results = bench_nnps(arrays)
print "###Timing Benchmarks for a Uniform Distribution###\n"
print results
def _make_2d_grid(self, name='fluid'):
n = 11
x, y = np.mgrid[-1:1:n*1j,-1:1:n*1j]
dx = 2.0/(n-1)
z = np.zeros_like(x)
x, y, z = x.ravel(), y.ravel(), z.ravel()
m = np.ones_like(x)
p = np.ones_like(x)*2.0
h = np.ones_like(x)*2*dx
u = np.ones_like(x)*0.1
pa = get_particle_array(name=name, x=x, y=y, z=z, h=h, m=m, p=p, u=u)
return pa
def setUp(self):
# create the particle arrays
L = 1.0; n = 100; dx = L/n; hdx = 1.5
_x = np.arange(dx/2, L, dx)
self.vol = vol = dx*dx
# fluid particles
xx, yy = np.meshgrid(_x, _x)
x = xx.ravel(); y = yy.ravel() # particle positions
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, h=h, m=m, V=V)
# channel particles
_y = np.arange(L+dx/2, L+dx/2 + 10*dx, dx)
xx, yy = np.meshgrid(_x, _y)
xtop = xx.ravel(); ytop = yy.ravel()
_y = np.arange(-dx/2, -dx/2-10*dx, -dx)
xx, yy = np.meshgrid(_x, _y)
xbot = xx.ravel(); ybot = yy.ravel()
x = np.concatenate( (xtop, xbot) )
y = np.concatenate( (ytop, ybot) )
h = np.ones_like(x) * hdx*dx
m = np.ones_like(x) * vol
V = np.zeros_like(x)
channel = get_particle_array(name='channel', x=x, y=y, h=h, m=m, V=V)
# particles and domain
self.particles = particles = [fluid, channel]
self.domain = domain = DomainManager(xmin=0, xmax=L, periodic_in_x=True)
self.kernel = get_compiled_kernel(Gaussian(dim=2))
def test_exact_extended_spatial_hash_variable_radius(self):
print ""
for key in self.dataset_variable_radius:
x, y, z, h = self.dataset_variable_radius[key]
pa = get_particle_array(x=x, y=y, z=z, h=h)
qid = randint(0,pa.get_number_of_particles()-1)
nn_bf = brute_force_neighbours(pa, pa.x[qid], pa.y[qid],
pa.z[qid], pa.h[qid])
exp = ExtendedSpatialHash(pa, 3, approximate = False)
nn_sp = UIntArray()
exp.nearest_neighbours(qid, nn_sp)
print " - " + key
assert equal(nn_bf, nn_sp)
def test_spatial_hash_variable_radius(self):
print ""
for key in self.dataset_variable_radius:
x, y, z, h = self.dataset_variable_radius[key]
pa = get_particle_array(x=x, y=y, z=z, h=h)
qid = randint(0,pa.get_number_of_particles()-1)
nn_bf = brute_force_neighbours(pa, pa.x[qid], pa.y[qid],
pa.z[qid], pa.h[qid])
sp = SpatialHashNNPS([pa])
nn_sp = UIntArray()
sp.set_context(0,0)
sp.get_nearest_neighbours(0,0,qid,nn_sp)
print " - " + key
assert equal(nn_bf, nn_sp)
def create_particles(hcp=False, **kwargs):
"Initial distribution using Hexagonal close packing of particles"
# create all particles
global dx
if hcp:
x, y, dx, dy, xmin, xmax, ymin, ymax = uniform_distribution.uniform_distribution_hcp2D(
dx=dx, xmin=-ghost_extent, xmax=Lx+ghost_extent,
ymin=-ghost_extent, ymax=Ly+ghost_extent)
else:
x, y, dx, dy, xmin, xmax, ymin, ymax = uniform_distribution.uniform_distribution_cubic2D(
dx=dx, xmin=-ghost_extent, xmax=Lx+ghost_extent,
ymin=-ghost_extent, ymax=Ly+ghost_extent)
x = x.ravel(); y = y.ravel()
# create the basic particle array
solid = get_particle_array(name='solid', x=x, y=y)
# now sort out the interior from all particles
indices = _get_interior(solid.x, solid.y)
fluid = solid.extract_particles( indices )
fluid.set_name('fluid')
solid.remove_particles( indices )
# sort out the obstacle from the interior
indices = _get_obstacle(fluid.x, fluid.y)
obstacle = fluid.extract_particles( indices )
obstacle.set_name('obstacle')
fluid.remove_particles(indices)
print "SPHERIC benchmark 6 :: Re = %d, nfluid = %d, nsolid=%d, nobstacle = %d, dt = %g"%(
Re, fluid.get_number_of_particles(),
solid.get_number_of_particles(),
obstacle.get_number_of_particles(), dt)
# setup requisite particle properties and initial conditions
if hcp:
wij_sum = uniform_distribution.get_number_density_hcp(dx, dy, kernel, h0)
volume = 1./wij_sum
else:
volume = dx*dy
particles = _setup_particle_properties([fluid, solid, obstacle], volume=volume)
return particles
def bench_random_distribution():
arrays = []
for numPoints in _numPoints:
dx = numpy.power( 1./numPoints, 1.0/3.0 )
xa = random.random(numPoints)
ya = random.random(numPoints)
za = random.random(numPoints)
ha = numpy.ones_like(xa) * 2*dx
gida = numpy.arange(numPoints).astype(numpy.uint32)
# create the particle array
pa = get_particle_array(x=xa, y=ya, z=za, h=ha, gid=gida)
arrays.append(pa)
results = bench_nnps(arrays)
print "###Timing Benchmarks for a Random Distribution###\n"
print results
def load(fname):
""" Load and return data from an output (.npz) file dumped by PySPH.
For output file version 1, the function returns a dictionary with
the keys:
solver_data : Solver constants at the time of output like time,
time step and iteration count.
arrays : ParticleArrays keyed on names with the ParticleArray
object as value.
"""
from pysph.base.utils import get_particle_array
data = numpy.load(fname)
ret = {"arrays":{}}
if not 'version' in data.files:
msg = "Wrong file type! No version nnumber recorded."
raise RuntimeError(msg)
version = data['version']
if version == 1:
arrays = data["arrays"]
arrays.shape = (1,)
arrays = arrays[0]
solver_data = data["solver_data"]
solver_data.shape = (1,)
solver_data = solver_data[0]
for array_name in arrays:
array = get_particle_array(name=array_name,
**arrays[array_name])
ret["arrays"][array_name] = array
ret["solver_data"] = solver_data
else:
raise RuntimeError("Version not understood!")
return ret
def test_spatial_hash_large_num_cells(self):
'''
Tests SpatialHashNNPS for large number of cells.
Fails with a segmentation fault.
'''
print ""
for key in self.dataset_large_domain:
x, y, z, h = self.dataset_large_domain[key]
pa = get_particle_array(x=x, y=y, z=z, h=h)
qid = randint(0,pa.get_number_of_particles()-1)
nn_bf = brute_force_neighbours(pa, pa.x[qid], pa.y[qid],
pa.z[qid], pa.h[qid])
sp = SpatialHashNNPS([pa])
nn_sp = UIntArray()
sp.set_context(0,0)
sp.get_nearest_neighbours(0,0,qid,nn_sp)
print " - " + key
assert equal(nn_bf, nn_sp)
def get_plate_particles():
x = numpy.arange(plate_start, plate_end+dx, dx)
y = numpy.zeros_like(x)
# normals and tangents
tx = numpy.ones_like(x)
ty = numpy.zeros_like(x)
tz = numpy.zeros_like(x)
ny = numpy.ones_like(x)
nx = numpy.zeros_like(x)
nz = numpy.zeros_like(x)
pa = get_particle_array(name='plate', x=x, y=y, tx=tx, ty=ty, tz=tz,
nx=nx, ny=ny, nz=nz)
pa.m[:] = m0
return pa
def _create_particle_array(self, x, y, z):
xr = x.ravel()
yr = y.ravel()
zr = z.ravel()
self.x, self.y, self.z = x.squeeze(), y.squeeze(), z.squeeze()
hmax = self._get_max_h_in_arrays()
h = hmax*np.ones_like(xr)
prop = np.zeros_like(xr)
pa = get_particle_array(
name='interpolate',
x=xr, y=yr, z=zr, h=h,
number_density=np.zeros_like(xr),
prop=prop,
grad_x=np.zeros_like(xr),
grad_y=np.zeros_like(xr),
grad_z=np.zeros_like(xr)
)
return pa
def test_linked_list_nnps_large_num_cells(self):
'''
Tests LinkedListNNPS for large number of cells.
Fails with a malloc error
'''
print ""
for key in self.dataset_large_domain:
x, y, z, h = self.dataset_large_domain[key]
pa = get_particle_array(x=x, y=y, z=z, h=h)
qid = randint(0,pa.get_number_of_particles()-1)
nn_bf = brute_force_neighbours(pa, pa.x[qid], pa.y[qid],
pa.z[qid], pa.h[qid])
r = 1/1.101
sp = LinkedListNNPS(dim=3, particles=[pa], radius_scale=r, cache=True)
nn_sp = UIntArray()
sp.set_context(0,0)
sp.get_nearest_particles(0,0,qid,nn_sp)
print " - " + key
assert equal(nn_bf, nn_sp)
def get_bar_particles():
xarr = numpy.arange(-bar_width/2.0, bar_width/2.0 + dx, dx)
yarr = numpy.arange(4*dx, 0.0254 + 4*dx, dx)
x,y = numpy.meshgrid( xarr, yarr )
x, y = x.ravel(), y.ravel()
print 'Number of bar particles: ', len(x)
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * r0
rhof = numpy.ones_like(x) * r0
csf = numpy.ones_like(x) * ss
z = numpy.zeros_like(x)
pa = get_particle_array(name="bar",
x=x, y=y, h=hf, m=mf, rho=rhof, cs=csf,
e=z)
# negative fluid particles
pa.v[:]=-200
return pa
def test_update_minmax_cl(self):
backend = 'opencl'
check_import(backend)
self.setup()
# Given
x = [0.0, -1.0, 2.0, 3.0]
y = [0.0, 1.0, -2.0, 3.0]
z = [0.0, 1.0, 2.0, -3.0]
h = [4.0, 1.0, 2.0, 3.0]
pa = get_particle_array(x=x, y=y, z=z, h=h)
h = DeviceHelper(pa, backend=backend)
pa.set_device_helper(h)
# When
h.update_minmax_cl(['x', 'y', 'z', 'h'])
# Then
assert h.x.minimum == -1.0
assert h.x.maximum == 3.0
assert h.y.minimum == -2.0
assert h.y.maximum == 3.0
assert h.z.minimum == -3.0
assert h.z.maximum == 2.0
assert h.h.minimum == 1.0
assert h.h.maximum == 4.0
# When
h.x.maximum, h.x.minimum = 100., 100.
h.y.maximum, h.y.minimum = 100., 100.
h.z.maximum, h.z.minimum = 100., 100.
h.h.maximum, h.h.minimum = 100., 100.
h.update_minmax_cl(['x', 'y', 'z', 'h'], only_min=True)
# Then
assert h.x.minimum == -1.0
assert h.x.maximum == 100.0
assert h.y.minimum == -2.0
assert h.y.maximum == 100.0
assert h.z.minimum == -3.0
assert h.z.maximum == 100.0
assert h.h.minimum == 1.0
assert h.h.maximum == 100.0
# When
h.x.maximum, h.x.minimum = 100., 100.
h.y.maximum, h.y.minimum = 100., 100.
h.z.maximum, h.z.minimum = 100., 100.
h.h.maximum, h.h.minimum = 100., 100.
h.update_minmax_cl(['x', 'y', 'z', 'h'], only_max=True)
# Then
assert h.x.minimum == 100.0
assert h.x.maximum == 3.0
assert h.y.minimum == 100.0
assert h.y.maximum == 3.0
assert h.z.minimum == 100.0
assert h.z.maximum == 2.0
assert h.h.minimum == 100.0
assert h.h.maximum == 4.0
def get_tsunami_geometry(dx_solid=0.01,
dx_fluid=0.01,
r_solid=100.0,
r_fluid=100.0,
hdx=1.3,
l_wall=9.5,
h_wall=4.0,
angle=26.565051,
h_fluid=3.5,
l_obstacle=0.91,
flat_l=2.25):
x1, y1, x2, y2 = get_beach_geometry_2d(dx_solid, l_wall, h_wall, flat_l,
angle, 4)
wall_x = np.concatenate([x1, x2])
wall_y = np.concatenate([y1, y2])
r_wall = np.ones_like(wall_x) * r_solid
m_wall = r_wall * dx_solid * dx_solid
h_w = np.ones_like(wall_x) * dx_solid * hdx
wall = get_particle_array(name='wall',
x=wall_x,
y=wall_y,
h=h_w,
rho=r_wall,
m=m_wall)
theta = np.pi * angle / 180.0
h_obstacle = l_obstacle * np.tan(theta)
obstacle_x1, obstacle_y1 = get_2d_block(dx_solid, l_obstacle, h_obstacle)
obstacle_x = []
obstacle_y = []
for x, y in zip(obstacle_x1, obstacle_y1):
if (y >= (-x * np.tan(theta))):
obstacle_x.append(x)
obstacle_y.append(y)
x_translate = (l_wall - flat_l) * 0.8
y_translate = x_translate * np.tan(theta)
obstacle_x = np.asarray(obstacle_x) - x_translate
obstacle_y = np.asarray(obstacle_y) + y_translate + dx_solid
h_obstacle = np.ones_like(obstacle_x) * dx_solid * hdx
r_obstacle = np.ones_like(obstacle_x) * r_solid
m_obstacle = r_obstacle * dx_solid * dx_solid
obstacle = get_particle_array(name='obstacle',
x=obstacle_x,
y=obstacle_y,
rho=r_obstacle,
m=m_obstacle,
h=h_obstacle)
fluid_center = np.array([flat_l - l_wall / 2.0, h_fluid / 2.0])
x_fluid, y_fluid = get_2d_block(dx_fluid, l_wall, h_fluid, fluid_center)
x3 = []
y3 = []
for i, xi in enumerate(x_fluid):
if y_fluid[i] >= np.tan(-theta) * xi:
x3.append(xi)
y3.append(y_fluid[i])
fluid_x = np.array(x3)
fluid_y = np.array(y3)
h_f = np.ones_like(fluid_x) * dx_fluid * hdx
r_f = np.ones_like(fluid_x) * r_fluid
m_f = r_f * dx_fluid * dx_fluid
fluid = get_particle_array(name='fluid',
x=fluid_x,
y=fluid_y,
h=h_f,
m=m_f,
rho=r_f)
remove_overlap_particles(fluid, obstacle, dx_solid * 1.1, 2)
remove_overlap_particles(fluid, wall, dx_solid * 1.1, 2)
return fluid, wall, obstacle
def get_plate_particles():
gmsh.initialize()
gmsh.open('t.msh')
# Launch the GUI to see the results:
# if '-nopopup' not in sys.argv:
# gmsh.fltk.run()
nodeTags, nodesCoord, parametricCoord = gmsh.model.mesh.getNodes()
liquid_x = nodesCoord[1::3]
liquid_y = nodesCoord[2::3]
liquid_z = nodesCoord[0::3]
liquid_y = liquid_y + abs(min(liquid_y))
liquid_x = liquid_x + abs(min(liquid_x))
liquid_x = liquid_x[0::5] * 1e-3 - 0.002
liquid_y = liquid_y[0::5] * 1e-3 - 0.1
liquid_z = liquid_z[0::5] * 1e-3 - 0.005
print('%d Target particles' % len(liquid_x))
hf = numpy.ones_like(liquid_x) * h
mf = numpy.ones_like(liquid_x) * dx * dy * dz * ro1
rhof = numpy.ones_like(liquid_x) * ro1
csf = numpy.ones_like(liquid_x) * cs1
pa = plate = get_particle_array(name="plate",
x=liquid_x,
y=liquid_y,
z=liquid_z,
h=hf,
m=mf,
rho=rhof,
cs=csf)
# add requisite properties
# sound speed etc.
add_properties(pa, 'e')
# velocity gradient properties
add_properties(pa, 'v00', 'v01', 'v02', 'v10', 'v11', 'v12', 'v20', 'v21',
'v22')
# artificial stress properties
add_properties(pa, 'r00', 'r01', 'r02', 'r11', 'r12', 'r22')
# deviatoric stress components
add_properties(pa, 's00', 's01', 's02', 's11', 's12', 's22')
# deviatoric stress accelerations
add_properties(pa, 'as00', 'as01', 'as02', 'as11', 'as12', 'as22')
# deviatoric stress initial values
add_properties(pa, 's000', 's010', 's020', 's110', 's120', 's220')
# standard acceleration variables
add_properties(pa, 'arho', 'au', 'av', 'aw', 'ax', 'ay', 'az', 'ae')
# initial values
add_properties(pa, 'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'e0')
pa.add_constant('G', G1)
pa.add_constant('n', 4)
kernel = Gaussian(dim=3)
wdeltap = kernel.kernel(rij=dx, h=hdx * dx)
pa.add_constant('wdeltap', wdeltap)
# load balancing properties
pa.set_lb_props(list(pa.properties.keys()))
# removed S_00 and similar components
plate.v[:] = 0.0
return plate
# expression for the particle volume. As far as the summation density
# test is concerned, the value will be uniform but not equal to 1. To
# reproduce a density profile of 1, we need to estimate the kernel sum
# or number density of the distribution based on the kernel
wij_sum_estimate = get_number_density_hcp(dx, dy, k, h0)
volume = 1. / wij_sum_estimate
print('Volume estimates :: dx^2 = %g, Number density = %g' % (dx * dy, volume))
x = x.ravel()
y = y.ravel()
h = numpy.ones_like(x) * h0
m = numpy.ones_like(x) * volume
wij = numpy.zeros_like(x)
# 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,
def create_ghost(self, pa_arr, inlet=True):
"""Creates ghosts for the given inlet/outlet particles
Parameters
-----------
pa_arr : Particle array
particles array for which ghost is required
inlet : bool
if True, inlet info will be used for ghost creation
"""
xref, yref, zref = 0.0, 0.0, 0.0
xn, yn, zn = 0.0, 0.0, 0.0
has_ghost = True
if inlet:
for info in self.inletinfo:
if info.pa_name == pa_arr.name:
xref = info.refpoint[0]
yref = info.refpoint[1]
zref = info.refpoint[2]
xn = info.normal[0]
yn = info.normal[1]
zn = info.normal[2]
has_ghost = info.has_ghost
break
if not inlet:
for info in self.outletinfo:
if info.pa_name == pa_arr.name:
xref = info.refpoint[0]
yref = info.refpoint[1]
zref = info.refpoint[2]
xn = info.normal[0]
yn = info.normal[1]
zn = info.normal[2]
has_ghost = info.has_ghost
break
if not has_ghost:
return None
x = pa_arr.x
y = pa_arr.y
z = pa_arr.z
xij = x - xref
yij = y - yref
zij = z - zref
disp = xij * xn + yij * yn + zij * zn
x = x - 2 * disp * xn
y = y - 2 * disp * yn
z = z - 2 * disp * zn
m = pa_arr.m
h = pa_arr.h
rho = pa_arr.rho
u = pa_arr.u
name = ''
if inlet:
name = self.inlet_pairs[pa_arr.name]
else:
name = self.outlet_pairs[pa_arr.name]
ghost_pa = get_particle_array(name=name,
m=m,
x=x,
y=y,
h=h,
u=u,
p=0.0,
rho=rho)
return ghost_pa
def create_particles(self):
ghost_extent = (nghost_layers + 0.5) * dx
x, y = numpy.mgrid[dxb2:domain_width:dx,
-ghost_extent:domain_height + ghost_extent:dy]
x = x.ravel()
y = y.ravel()
m = numpy.ones_like(x) * volume * rho0
rho = numpy.ones_like(x) * rho0
p = numpy.ones_like(x) * p0
h = numpy.ones_like(x) * h0
cs = numpy.ones_like(x) * c0
# additional properties required for the fluid.
additional_props = [
# volume inverse or number density
'V',
'pi00',
'pi01',
'pi10',
'pi11',
# color and gradients
'color',
'scolor',
'cx',
'cy',
'cz',
'cx2',
'cy2',
'cz2',
# discretized interface normals and dirac delta
'nx',
'ny',
'nz',
'ddelta',
# interface curvature
'kappa',
'nu',
'alpha',
# filtered velocities
'uf',
'vf',
'wf',
# transport velocities
'uhat',
'vhat',
'what',
'auhat',
'avhat',
'awhat',
# imposed accelerations on the solid wall
'ax',
'ay',
'az',
'wij',
# velocity of magnitude squared
'vmag2',
# variable to indicate reliable normals and normalizing
# constant
'N',
'wij_sum',
'wg',
'ug',
'vg'
]
# get the fluid particle array
fluid = get_particle_array(name='fluid',
x=x,
y=y,
h=h,
m=m,
rho=rho,
cs=cs,
p=p,
additional_props=additional_props)
# set the fluid velocity with respect to the sinusoidal
# perturbation
fluid.u[:] = -U
fluid.N[:] = 0.0
fluid.nu[:] = nu
fluid.alpha[:] = sigma
mode = 1
for i in range(len(fluid.x)):
ang = 2 * numpy.pi * fluid.x[i] / (mode * domain_width)
if fluid.y[
i] >= domain_height / 2 + psi0 * domain_height * numpy.sin(
ang):
fluid.u[i] = U
fluid.color[i] = 1.0
# extract the top and bottom boundary particles
indices = numpy.where(fluid.y > domain_height)[0]
wall = fluid.extract_particles(indices)
fluid.remove_particles(indices)
indices = numpy.where(fluid.y < 0)[0]
bottom = fluid.extract_particles(indices)
fluid.remove_particles(indices)
# concatenate the two boundaries
wall.append_parray(bottom)
wall.set_name('wall')
# set the number density initially for all particles
fluid.V[:] = 1. / volume
wall.V[:] = 1. / volume
for i in range(len(wall.x)):
if wall.y[i] > 0.5:
wall.color[i] = 1.0
else:
wall.color[i] = 0.0
# set additional output arrays for the fluid
fluid.add_output_arrays([
'V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta', 'p', 'rho', 'au',
'av'
])
wall.add_output_arrays([
'V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta', 'p', 'rho', 'au',
'av'
])
print(
"2D KHI with %d fluid particles and %d wall particles" %
(fluid.get_number_of_particles(), wall.get_number_of_particles()))
return [fluid, wall]
def create_particles(**kwargs):
ghost_extent = (nghost_layers + 0.5)*dx
x, y = numpy.mgrid[ dxb2:domain_width:dx, -ghost_extent:domain_height+ghost_extent:dy ]
x = x.ravel(); y = y.ravel()
m = numpy.ones_like(x) * volume * rho0
rho = numpy.ones_like(x) * rho0
h = numpy.ones_like(x) * h0
cs = numpy.ones_like(x) * c0
# additional properties required for the fluid.
additional_props = [
# volume inverse or number density
'V',
# color and gradients
'color', 'scolor', 'cx', 'cy', 'cz', 'cx2', 'cy2', 'cz2',
# discretized interface normals and dirac delta
'nx', 'ny', 'nz', 'ddelta',
# interface curvature
'kappa',
# filtered velocities
'uf', 'vf', 'wf',
# transport velocities
'uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat',
# imposed accelerations on the solid wall
'ax', 'ay', 'az', 'wij',
# velocity of magnitude squared
'vmag2',
# variable to indicate reliable normals and normalizing
# constant
'N', 'wij_sum',
]
# get the fluid particle array
fluid = get_particle_array(
name='fluid', x=x, y=y, h=h, m=m, rho=rho, cs=cs,
additional_props=additional_props)
# set the fluid velocity with respect to the sinusoidal
# perturbation
fluid.u[:] = -U
mode = 1
for i in range(len(fluid.x)):
if fluid.y[i] > domain_height/2 + psi0*domain_height*numpy.sin(2*numpy.pi*fluid.x[i]/(mode*domain_width)):
fluid.u[i] = U
fluid.color[i] = 1
fluid.rho[i] = rho1
fluid.m[i] = volume*rho1
else:
fluid.rho[i] = rho2
fluid.m[i] = rho2/rho1*volume*rho2
# extract the top and bottom boundary particles
indices = numpy.where( fluid.y > domain_height )[0]
wall = fluid.extract_particles( indices )
fluid.remove_particles( indices )
indices = numpy.where( fluid.y < 0 )[0]
bottom = fluid.extract_particles( indices )
fluid.remove_particles( indices )
# concatenate the two boundaries
wall.append_parray( bottom )
wall.set_name( 'wall' )
# set the number density initially for all particles
fluid.V[:] = 1./volume
wall.V[:] = 1./volume
# set additional output arrays for the fluid
fluid.add_output_arrays(['V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta',
'kappa', 'N', 'p', 'rho'])
print "2D KHI with %d fluid particles and %d wall particles"%(
fluid.get_number_of_particles(), wall.get_number_of_particles())
return [fluid, wall]
def get_plate_particles():
xarr = numpy.arange(0, 0.002 + dx, dx)
yarr = numpy.arange(-0.020, 0.02 + dx, dx)
zarr = numpy.arange(-0.02, 0.02 + dx, dx)
x, y, z = numpy.mgrid[0:0.002 + dx:dx, -0.02:0.02 + dx:dx,
-0.02:0.02 + dx:dx]
x = x.ravel()
y = y.ravel()
z = z.ravel()
print('%d Target particles' % len(x))
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * dz * ro1
rhof = numpy.ones_like(x) * ro1
csf = numpy.ones_like(x) * cs1
pa = plate = get_particle_array(name="plate",
x=x,
y=y,
z=z,
h=hf,
m=mf,
rho=rhof,
cs=csf)
# add requisite properties
# sound speed etc.
add_properties(pa, 'e')
# velocity gradient properties
add_properties(pa, 'v00', 'v01', 'v02', 'v10', 'v11', 'v12', 'v20', 'v21',
'v22')
# artificial stress properties
add_properties(pa, 'r00', 'r01', 'r02', 'r11', 'r12', 'r22')
# deviatoric stress components
add_properties(pa, 's00', 's01', 's02', 's11', 's12', 's22')
# deviatoric stress accelerations
add_properties(pa, 'as00', 'as01', 'as02', 'as11', 'as12', 'as22')
# deviatoric stress initial values
add_properties(pa, 's000', 's010', 's020', 's110', 's120', 's220')
# standard acceleration variables
add_properties(pa, 'arho', 'au', 'av', 'aw', 'ax', 'ay', 'az', 'ae')
# initial values
add_properties(pa, 'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'e0')
pa.add_constant('G', G1)
pa.add_constant('n', 4)
kernel = Gaussian(dim=3)
wdeltap = kernel.kernel(rij=dx, h=hdx * dx)
pa.add_constant('wdeltap', wdeltap)
# load balancing properties
pa.set_lb_props(list(pa.properties.keys()))
# removed S_00 and similar components
plate.v[:] = 0.0
return plate
def create_particles(**kwargs):
_x = np.arange( dx/2, Lx, dx )
# create the fluid particles
_y = np.arange( dx/2, Ly, dx )
x, y = np.meshgrid(_x, _y); fx = x.ravel(); fy = y.ravel()
# create the channel particles at the top
_y = np.arange(Ly+dx/2, Ly+dx/2+ghost_extent, dx)
x, y = np.meshgrid(_x, _y); tx = x.ravel(); ty = y.ravel()
# create the channel particles at the bottom
_y = np.arange(-dx/2, -dx/2-ghost_extent, -dx)
x, y = np.meshgrid(_x, _y); bx = x.ravel(); by = y.ravel()
# concatenate the top and bottom arrays
cx = np.concatenate( (tx, bx) )
cy = np.concatenate( (ty, by) )
# create the arrays
channel = get_particle_array(name='channel', x=cx, y=cy)
fluid = get_particle_array(name='fluid', x=fx, y=fy)
print "Poiseuille flow :: Re = %g, nfluid = %d, nchannel=%d, dt = %g"%(
Re, fluid.get_number_of_particles(),
channel.get_number_of_particles(), dt)
# add requisite properties to the arrays:
# particle volume
fluid.add_property('V')
channel.add_property('V' )
# advection velocities and accelerations
for name in ('uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat', 'au', 'av', 'aw'):
fluid.add_property(name)
# kernel summation correction for the channel
channel.add_property('wij')
# imposed accelerations on the solid
channel.add_property('ax')
channel.add_property('ay')
channel.add_property('az')
# Shepard filtered velocities for the fluid
for name in ['uf', 'vf', 'wf']:
fluid.add_property(name)
# magnitude of velocity
fluid.add_property('vmag2')
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of rho0
fluid.m[:] = volume * rho0
channel.m[:] = volume * rho0
# volume is set as dx^2
fluid.V[:] = 1./volume
channel.V[:] = 1./volume
# smoothing lengths
fluid.h[:] = hdx * dx
channel.h[:] = hdx * dx
# load balancing props
fluid.set_lb_props( fluid.properties.keys() )
channel.set_lb_props( channel.properties.keys() )
# return the particle list
return [fluid, channel]
def create_particles(self):
# create the particles
dx = self.dx
_x = np.arange(dx / 2, L, dx)
x, y = np.meshgrid(_x, _x)
if self.options.init is not None:
fname = self.options.init
from pysph.solver.utils import load
data = load(fname)
_f = data['arrays']['fluid']
x, y = _f.x.copy(), _f.y.copy()
if self.options.perturb > 0:
np.random.seed(1)
factor = dx * self.options.perturb
x += np.random.random(x.shape) * factor
y += np.random.random(x.shape) * factor
# Initialize
m = self.volume * rho0
h = self.hdx * dx
re = self.options.re
b = -8.0 * pi * pi / re
u0, v0, p0 = exact_solution(U=U, b=b, t=0, x=x, y=y)
color0 = cos(2 * pi * x) * cos(4 * pi * y)
# create the arrays
fluid = get_particle_array(name='fluid',
x=x,
y=y,
m=m,
h=h,
u=u0,
v=v0,
rho=rho0,
p=p0,
color=color0)
self.scheme.setup_properties([fluid])
print("Taylor green vortex problem :: nfluid = %d, dt = %g" %
(fluid.get_number_of_particles(), self.dt))
# volume is set as dx^2
if self.options.scheme == 'tvf':
fluid.V[:] = 1. / self.volume
if self.options.scheme == 'iisph':
# These are needed to update the ghost particle properties.
nfp = fluid.get_number_of_particles()
fluid.orig_idx[:] = np.arange(nfp)
fluid.add_output_arrays(['orig_idx'])
corr = self.kernel_correction
if corr in ['mixed', 'crksph']:
fluid.add_property('cwij')
if corr == 'mixed' or corr == 'gradient':
fluid.add_property('m_mat', stride=9)
fluid.add_property('dw_gamma', stride=3)
elif corr == 'crksph':
fluid.add_property('ai')
fluid.add_property('gradbi', stride=4)
for prop in ['gradai', 'bi']:
fluid.add_property(prop, stride=2)
return [fluid]
def get_wavespaddle_geometry(hdx=1.5,
dx_f=0.1,
dx_s=0.05,
r_f=100.,
r_s=100.,
length=3.75,
height=0.3,
flat_l=1.,
angle=4.2364,
h_fluid=0.2,
obstacle_side=0.06):
x1, y1, x2, y2 = get_beach_geometry_2d(dx_s, length, height, flat_l, angle,
3)
r1 = np.ones_like(x1) * r_s
m1 = r1 * dx_s * dx_s
h1 = np.ones_like(x1) * hdx * dx_s
cs1 = np.zeros_like(x1)
rad1 = np.ones_like(x1) * dx_s
wall = get_particle_array(name='wall',
x=x1,
y=y1,
rho=r1,
m=m1,
h=h1,
cs=cs1,
rad_s=rad1)
r2 = np.ones_like(x2) * r_s
m2 = r2 * dx_s * dx_s
h2 = np.ones_like(x2) * hdx * dx_s
paddle = get_particle_array(name='paddle', x=x2, y=y2, rho=r2, m=m2, h=h2)
fluid_center = np.array([flat_l - length / 2.0, h_fluid / 2.0])
x_fluid, y_fluid = get_2d_block(dx_f, length, h_fluid, fluid_center)
x3 = []
y3 = []
theta = np.pi * angle / 180.0
for i, xi in enumerate(x_fluid):
if y_fluid[i] >= np.tan(-theta) * xi:
x3.append(xi)
y3.append(y_fluid[i])
x3 = np.array(x3)
y3 = np.array(y3)
r3 = np.ones_like(x3) * r_f
m3 = r3 * dx_f * dx_f
h3 = np.ones_like(x3) * hdx * dx_f
cs3 = np.zeros_like(x3)
rad3 = np.ones_like(x3) * dx_f
fluid = get_particle_array(name='fluid', x=x3, y=y3, rho=r3, m=m3, h=h3)
square_center = np.array([-0.38, 0.16])
x4, y4 = get_2d_block(dx_s, obstacle_side, obstacle_side, square_center)
b1 = np.zeros_like(x4, dtype=int)
square_center = np.array([-0.7, 0.16])
x5, y5 = get_2d_block(dx_s, obstacle_side, obstacle_side, square_center)
b2 = np.ones_like(x5, dtype=int)
square_center = np.array([-1.56, 0.22])
x6, y6 = get_2d_block(dx_s, obstacle_side, obstacle_side, square_center)
b3 = np.ones_like(x5, dtype=int) * 2
b = np.concatenate([b1, b2, b3])
x4 = np.concatenate([x4, x5, x6])
y4 = np.concatenate([y4, y5, y6])
r4 = np.ones_like(x4) * r_s * 0.5
m4 = r4 * dx_s * dx_s
h4 = np.ones_like(x4) * hdx * dx_s
cs4 = np.zeros_like(x4)
rad4 = np.ones_like(x4) * dx_s
obstacle = get_particle_array_rigid_body(name='obstacle',
x=x4,
y=y4,
h=h4,
rho=r4,
m=m4,
cs=cs4,
rad_s=rad4,
body_id=b)
remove_overlap_particles(fluid, wall, dx_s, 2)
remove_overlap_particles(fluid, paddle, dx_s, 2)
remove_overlap_particles(fluid, obstacle, dx_s, 2)
return fluid, wall, paddle, obstacle
def create_particles(self):
hdx = self.hdx
dx = self.dx
ghost_extent = 5 * dx
# create all the particles
_x = np.arange(-ghost_extent - dx / 2, L + ghost_extent + dx / 2, dx)
x, y = np.meshgrid(_x, _x)
x = x.ravel()
y = y.ravel()
# sort out the fluid and the solid
indices = []
for i in range(x.size):
if ((x[i] > 0.0) and (x[i] < L)):
if ((y[i] > 0.0) and (y[i] < L)):
indices.append(i)
# create the arrays
solid = get_particle_array(name='solid', x=x, y=y)
# remove the fluid particles from the solid
fluid = solid.extract_particles(indices)
fluid.set_name('fluid')
solid.remove_particles(indices)
# add requisite properties to the arrays:
self.scheme.setup_properties([fluid, solid])
disp_vals = (self.re, self.dt, self.dx)
print("Lid driven cavity :: Re = %d, dt = %g, dx = %g" % disp_vals)
temp = self.Umax*self.h0/self.nu
print('Check Factor = ', temp)
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of rho0
fluid.m[:] = volume * rho0
solid.m[:] = volume * rho0
# Set a reference rho also, some schemes will overwrite this with a
# summation density.
fluid.rho[:] = rho0
solid.rho[:] = rho0
# smoothing lengths
fluid.h[:] = hdx * dx
solid.h[:] = hdx * dx
# imposed horizontal velocity on the lid
solid.u[:] = 0.0
solid.v[:] = 0.0
for i in range(solid.get_number_of_particles()):
if solid.y[i] > L:
solid.u[i] = Umax
# volume is set as dx^2
fluid.V[:] = 1. / volume
solid.V[:] = 1. / volume
fluid.lmda[:] = 1.0
return [fluid, solid]
update_nstlist = True
else:
update_nstlist = False
# update_dyn has the priority over update_nstlist
update = False
if update_dyn:
update = True
else:
update = update_nstlist
if update:
print "Neighbor search",nl_updates
pa = get_particle_array(name='prot', x=rx, y=ry, z=rz, h=1.0) #h=1.0 must not be changed as it affects radius_scale in nnps.LinkedListNNPS
nps = nnps.LinkedListNNPS(dim=3, particles=[pa], radius_scale=rcut2)
#nps = nnps.SpatialHashNNPS(dim=3, particles=[pa], radius_scale=rcut2)
src_index = 0
dst_index = 0
neighbors = []
tot = 0
for i in range(nca):
nbrs = UIntArray()
nps.get_nearest_particles(src_index, dst_index, i, nbrs)
tmp = nbrs.get_npy_array().tolist()
# patch
def create_particles(empty=False, **kwargs):
# create all the particles
_x = np.arange( -ghost_extent - dx/2, Lx + ghost_extent + dx/2, dx )
_y = np.arange( -ghost_extent - dx/2, Ly + ghost_extent + dx/2, dx )
x, y = np.meshgrid(_x, _y); x = x.ravel(); y = y.ravel()
# sort out the fluid and the solid
indices = []
for i in range(x.size):
if ( (x[i] > 0.0) and (x[i] < Lx) ):
if ( (y[i] > 0.0) and (y[i] < Ly) ):
indices.append(i)
# create the arrays
solid = get_particle_array(name='solid', x=x, y=y)
# remove the fluid particles from the solid
fluid = solid.extract_particles(indices); fluid.set_name('fluid')
solid.remove_particles(indices)
# sort out the two fluid phases
indices = []
for i in range(fluid.get_number_of_particles()):
if fluid.y[i] > 1 - 0.15*np.sin(2*np.pi*fluid.x[i]):
indices.append(i)
fluid1 = fluid.extract_particles(indices); fluid1.set_name('fluid1')
fluid2 = fluid
fluid2.set_name('fluid2')
fluid2.remove_particles(indices)
fluid1.rho[:] = rho1
fluid2.rho[:] = rho2
print "Rayleigh Taylor Instability problem :: Re = %d, nfluid = %d, nsolid=%d, dt = %g"%(
Re, fluid1.get_number_of_particles() + fluid2.get_number_of_particles(),
solid.get_number_of_particles(), dt)
# add requisite properties to the arrays:
# particle volume
fluid1.add_property('V')
fluid2.add_property('V')
solid.add_property('V' )
# advection velocities and accelerations
for name in ('uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat', 'au', 'av', 'aw'):
fluid1.add_property(name)
fluid2.add_property(name)
# kernel summation correction for the solid
solid.add_property('wij')
# imposed accelerations on the solid
solid.add_property('ax')
solid.add_property('ay')
solid.add_property('az')
# Shepard filtered velocities for the fluid
for name in ['uf', 'vf', 'wf']:
fluid1.add_property(name)
fluid2.add_property(name)
# magnitude of velocity
fluid1.add_property('vmag2')
fluid2.add_property('vmag2')
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of each phase
fluid1.m[:] = volume * rho1
fluid2.m[:] = volume * rho2
# volume is set as dx^2
fluid1.V[:] = 1./volume
fluid2.V[:] = 1./volume
solid.V[:] = 1./volume
# smoothing lengths
fluid1.h[:] = hdx * dx
fluid2.h[:] = hdx * dx
solid.h[:] = hdx * dx
# load balancing props
fluid1.set_lb_props( fluid1.properties.keys() )
fluid2.set_lb_props( fluid2.properties.keys() )
solid.set_lb_props( solid.properties.keys() )
# return the arrays
return [fluid1, fluid2, solid]
def create_particles(**kwargs):
#x,y = numpy.mgrid[-1.05:1.05+1e-4:dx, -0.105:0.105+1e-4:dx]
spacing = 0.041 # spacing = 2*5cm
x,y = numpy.mgrid[-ro:ro:dx, -ro:ro:dx]
x = x.ravel()
y = y.ravel()
d = (x*x+y*y)
keep = numpy.flatnonzero((ri*ri<=d) * (d<ro*ro))
x = x[keep]
y = y[keep]
x = numpy.concatenate([x-spacing,x+spacing])
y = numpy.concatenate([y,y])
print 'Ellastic Collision with %d particles'%(x.size)
print "Shear modulus G = %g, Young's modulus = %g, Poisson's ratio =%g"%(G,E,nu)
#print bdry, numpy.flatnonzero(bdry)
m = numpy.ones_like(x)*dx*dx
h = numpy.ones_like(x)*hdx*dx
rho = numpy.ones_like(x)
z = numpy.zeros_like(x)
p = 0.5*1.0*100*100*(1 - (x**2 + y**2))
cs = numpy.ones_like(x) * 10000.0
# u is set later
v = z
u_f = 0.059
p *= 0
h *= 1
# create the particle array
pa = get_particle_array(
name="solid", x=x+spacing, y=y, m=m, rho=rho, h=h,
p=p, cs=cs, u=z, v=v)
pa.cs[:] = c0
pa.u = pa.cs*u_f*(2*(x<0)-1)
# add requisite properties
# sound speed etc.
add_properties(pa, 'cs', 'e' )
# velocity gradient properties
add_properties(pa, 'v00', 'v01', 'v10', 'v11')
# artificial stress properties
add_properties(pa, 'r00', 'r01', 'r02', 'r11', 'r12', 'r22')
# deviatoric stress components
add_properties(pa, 's00', 's01', 's02', 's11', 's12', 's22')
# deviatoric stress accelerations
add_properties(pa, 'as00', 'as01', 'as02', 'as11', 'as12', 'as22')
# deviatoric stress initial values
add_properties(pa, 's000', 's010', 's020', 's110', 's120', 's220')
# standard acceleration variables
add_properties(pa, 'arho', 'au', 'av', 'aw', 'ax', 'ay', 'az', 'ae')
# initial values
add_properties(pa, 'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'e0')
# load balancing properties
pa.set_lb_props( pa.properties.keys() )
return [pa,]
def create_particles(self):
fluid_x, fluid_y = get_2d_block(
dx=dx, length=Lx, height=Ly, center=np.array([0., 0.]))
rho_fluid = np.ones_like(fluid_x) * rho2
m_fluid = rho_fluid * volume
h_fluid = np.ones_like(fluid_x) * h0
cs_fluid = np.ones_like(fluid_x) * c0
circle_x, circle_y = get_2d_circle(dx=dx, r=0.2,
center=np.array([0.0, 0.0]))
rho_circle = np.ones_like(circle_x) * rho1
m_circle = rho_circle * volume
h_circle = np.ones_like(circle_x) * h0
cs_circle = np.ones_like(circle_x) * c0
wall_x, wall_y = get_2d_block(dx=dx, length=Lx+6*dx, height=Ly+6*dx,
center=np.array([0., 0.]))
rho_wall = np.ones_like(wall_x) * rho2
m_wall = rho_wall * volume
h_wall = np.ones_like(wall_x) * h0
cs_wall = np.ones_like(wall_x) * c0
additional_props = ['V', 'color', 'scolor', 'cx', 'cy', 'cz',
'cx2', 'cy2', 'cz2', 'nx', 'ny', 'nz', 'ddelta',
'uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat',
'ax', 'ay', 'az', 'wij', 'vmag2', 'N', 'wij_sum',
'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0',
'kappa', 'arho', 'nu', 'wg', 'ug', 'vg',
'pi00', 'pi01', 'pi02', 'pi10', 'pi11', 'pi12',
'pi20', 'pi21', 'pi22']
gas = get_particle_array(
name='gas', x=fluid_x, y=fluid_y, h=h_fluid, m=m_fluid,
rho=rho_fluid, cs=cs_fluid, additional_props=additional_props)
gas.nu[:] = nu2
gas.color[:] = 0.0
liquid = get_particle_array(
name='liquid', x=circle_x, y=circle_y, h=h_circle, m=m_circle,
rho=rho_circle, cs=cs_circle, additional_props=additional_props)
liquid.nu[:] = nu1
liquid.color[:] = 1.0
wall = get_particle_array(
name='wall', x=wall_x, y=wall_y, h=h_wall, m=m_wall,
rho=rho_wall, cs=cs_wall, additional_props=additional_props)
wall.color[:] = 0.0
remove_overlap_particles(wall, liquid, dx_solid=dx, dim=2)
remove_overlap_particles(wall, gas, dx_solid=dx, dim=2)
remove_overlap_particles(gas, liquid, dx_solid=dx, dim=2)
gas.add_output_arrays(['V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta',
'kappa', 'N', 'scolor', 'p'])
liquid.add_output_arrays(['V', 'color', 'cx', 'cy', 'nx', 'ny',
'ddelta', 'kappa', 'N', 'scolor', 'p'])
wall.add_output_arrays(['V', 'color', 'cx', 'cy', 'nx', 'ny',
'ddelta', 'kappa', 'N', 'scolor', 'p'])
for i in range(len(gas.x)):
R = sqrt(r(gas.x[i], gas.y[i]) + 0.0001*gas.h[i]*gas.h[i])
f = np.exp(-R/r0)
gas.u[i] = v0*gas.x[i]*(1.0-(gas.y[i]*gas.y[i])/(r0*R))*f/r0
gas.v[i] = -v0*gas.y[i]*(1.0-(gas.x[i]*gas.x[i])/(r0*R))*f/r0
for i in range(len(liquid.x)):
R = sqrt(r(liquid.x[i], liquid.y[i]) +
0.0001*liquid.h[i]*liquid.h[i])
liquid.u[i] = v0*liquid.x[i] * \
(1.0 - (liquid.y[i]*liquid.y[i])/(r0*R))*f/r0
liquid.v[i] = -v0*liquid.y[i] * \
(1.0 - (liquid.x[i]*liquid.x[i])/(r0*R))*f/r0
return [liquid, gas, wall]
def create_particles(self):
# create all the particles
_x = np.arange(-ghost_extent - dx / 2, Lx + ghost_extent + dx / 2, dx)
_y = np.arange(-ghost_extent - dx / 2, Ly + ghost_extent + dx / 2, dx)
x, y = np.meshgrid(_x, _y)
x = x.ravel()
y = y.ravel()
# sort out the fluid and the solid
indices = []
for i in range(x.size):
if ((x[i] > 0.0) and (x[i] < Lx)):
if ((y[i] > 0.0) and (y[i] < Ly)):
indices.append(i)
# create the arrays
solid = get_particle_array(name='solid', x=x, y=y)
# remove the fluid particles from the solid
fluid = solid.extract_particles(indices)
fluid.set_name('fluid')
solid.remove_particles(indices)
# sort out the two fluid phases
indices = []
for i in range(fluid.get_number_of_particles()):
if fluid.y[i] > 1 - 0.15 * np.sin(2 * np.pi * fluid.x[i]):
indices.append(i)
fluid1 = fluid.extract_particles(indices)
fluid1.set_name('fluid1')
fluid2 = fluid
fluid2.set_name('fluid2')
fluid2.remove_particles(indices)
fluid1.rho[:] = rho1
fluid2.rho[:] = rho2
print(
"Rayleigh Taylor Instability problem :: Re = %d, nfluid = %d, nsolid=%d, dt = %g"
% (Re, fluid1.get_number_of_particles() +
fluid2.get_number_of_particles(),
solid.get_number_of_particles(), dt))
# add requisite properties to the arrays:
self.scheme.setup_properties([fluid1, fluid2, solid])
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of each phase
fluid1.m[:] = volume * rho1
fluid2.m[:] = volume * rho2
# volume is set as dx^2
fluid1.V[:] = 1. / volume
fluid2.V[:] = 1. / volume
solid.V[:] = 1. / volume
# smoothing lengths
fluid1.h[:] = hdx * dx
fluid2.h[:] = hdx * dx
solid.h[:] = hdx * dx
# return the arrays
return [fluid1, fluid2, solid]
def get_projectile_particles():
x, y = numpy.mgrid[-r:r:dx, -r:r:dx]
x = x.ravel()
y = y.ravel()
d = (x * x + y * y)
keep = numpy.flatnonzero(d <= r * r)
x = x[keep]
y = y[keep]
x = x - (r + 2 * dx)
print('%d Projectile particles' % len(x))
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * ro2
rhof = numpy.ones_like(x) * ro2
csf = numpy.ones_like(x) * cs2
z = numpy.zeros_like(x)
u = numpy.ones_like(x) * v_s
pa = projectile = get_particle_array(name="projectile",
x=x,
y=y,
h=hf,
m=mf,
rho=rhof,
cs=csf,
u=u)
# add requisite properties
# sound speed etc.
add_properties(pa, 'e')
# velocity gradient properties
add_properties(pa, 'v00', 'v01', 'v02', 'v10', 'v11', 'v12', 'v20', 'v21',
'v22')
# artificial stress properties
add_properties(pa, 'r00', 'r01', 'r02', 'r11', 'r12', 'r22')
# deviatoric stress components
add_properties(pa, 's00', 's01', 's02', 's11', 's12', 's22')
# deviatoric stress accelerations
add_properties(pa, 'as00', 'as01', 'as02', 'as11', 'as12', 'as22')
# deviatoric stress initial values
add_properties(pa, 's000', 's010', 's020', 's110', 's120', 's220')
# standard acceleration variables
add_properties(pa, 'arho', 'au', 'av', 'aw', 'ax', 'ay', 'az', 'ae')
# initial values
add_properties(pa, 'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'e0')
# load balancing properties
pa.set_lb_props(list(pa.properties.keys()))
return projectile
def create_particles(self):
# create all the particles
_x = np.arange( -ghost_extent - dx/2, Lx + ghost_extent + dx/2, dx )
_y = np.arange( -ghost_extent - dx/2, Ly + ghost_extent + dx/2, dx )
x, y = np.meshgrid(_x, _y); x = x.ravel(); y = y.ravel()
# sort out the fluid and the solid
indices = []
for i in range(x.size):
if ( (x[i] > 0.0) and (x[i] < Lx) ):
if ( (y[i] > 0.0) and (y[i] < Ly) ):
indices.append(i)
# create the arrays
solid = get_particle_array(name='solid', x=x, y=y)
# remove the fluid particles from the solid
fluid = solid.extract_particles(indices); fluid.set_name('fluid')
solid.remove_particles(indices)
# sort out the two fluid phases
indices = []
for i in range(fluid.get_number_of_particles()):
if fluid.y[i] > 1 - 0.15*np.sin(2*np.pi*fluid.x[i]):
indices.append(i)
fluid1 = fluid.extract_particles(indices);
fluid1.set_name('fluid1')
fluid2 = fluid
fluid2.set_name('fluid2')
fluid2.remove_particles(indices)
# add requisite properties to the arrays:
# self.scheme.setup_properties([solid])
materials = MixtureParticle(baseProperties={'V': 0, 'auhat': 0, 'avhat': 0, 'awhat': 0, 'what': 0, 'uhat': 0, 'vhat': 0, 'rho0': rho1, 'vmag2':0, 'p0': p1, 't0': 270, 't': 270})
print('props', materials.generateFullParticleProperties())
for propertyName in materials.generateFullParticleProperties():
solid.add_property(propertyName, type='double', default=materials.getPropertyValue(propertyName))
fluid1.add_property(propertyName, type='double', default=materials.getPropertyValue(propertyName))
fluid2.add_property(propertyName, type='double', default=materials.getPropertyValue(propertyName))
for prop in ['wg', 'wf', 'vf', 'vg', 'ug', 'uf', 'wij']:
solid.add_property(prop)
fluid1.rho[:] = rho1
fluid1.rho0[:] = rho1
fluid1.p[:] = p1
fluid1.p0[:] = p1
fluid2.rho[:] = rho2
fluid2.rho0[:] = rho2
fluid2.p[:] = p2
fluid2.p0[:] = p2
print("Rayleigh Taylor Instability problem :: Re = %d, nfluid = %d, nsolid=%d, dt = %g"%(
Re, fluid1.get_number_of_particles() + fluid2.get_number_of_particles(),
solid.get_number_of_particles(), dt))
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of each phase
fluid1.m[:] = volume * rho1
fluid2.m[:] = volume * rho2
# volume is set as dx^2
fluid1.V[:] = 1./volume
fluid2.V[:] = 1./volume
solid.V[:] = 1./volume
# smoothing lengths
fluid1.h[:] = hdx * dx
fluid2.h[:] = hdx * dx
solid.h[:] = hdx * dx
both_fluids = fluid1
both_fluids.append_parray(fluid2)
both_fluids.set_name('fluid')
# return the arrays
return [both_fluids, solid]
def setUp(self):
"""Default set-up used by all the tests
Particles with the following coordinates (x, y, z) are placed in a box
0 : -1.5 , 0.25 , 0.5
1 : 0.33 , -0.25, 0.25
2 : 1.25 , -1.25, 1.25
3 : 0.05 , 1.25 , -0.5
4 : -0.5 , 0.5 , -1.25
5 : -0.75, 0.75 , -1.25
6 : -1.25, 0.5 , 0.5
7 : 0.5 , 1.5 , -0.5
8 : 0.5 , -0.5 , 0.5
9 : 0.5 , 1.75 , -0.75
The cell size is set to 1. Valid cell indices and the
particles they contain are given below:
(-2, 0, 0) : particle 0, 6
(0, -1, 0) : particle 1, 8
(1, -2, 1) : particle 2
(0, 1, -1) : particle 3, 7, 9
(-1, 0, -2): particle 4, 5
"""
x = numpy.array(
[-1.5, 0.33, 1.25, 0.05, -0.5, -0.75, -1.25, 0.5, 0.5, 0.5])
y = numpy.array(
[0.25, -0.25, -1.25, 1.25, 0.5, 0.75, 0.5, 1.5, -0.5, 1.75])
z = numpy.array(
[0.5, 0.25, 1.25, -0.5, -1.25, -1.25, 0.5, -0.5, 0.5, -0.75])
# using a degenrate (h=0) array will set cell size to 1 for NNPS
h = numpy.zeros_like(x)
pa = get_particle_array(x=x, y=y, z=z, h=h)
self.dict_box_sort_nnps = nnps.DictBoxSortNNPS(dim=3,
particles=[pa],
radius_scale=1.0)
self.box_sort_nnps = nnps.BoxSortNNPS(dim=3,
particles=[pa],
radius_scale=1.0)
self.ll_nnps = nnps.LinkedListNNPS(dim=3,
particles=[pa],
radius_scale=1.0)
self.sp_hash_nnps = nnps.SpatialHashNNPS(dim=3,
particles=[pa],
radius_scale=1.0)
self.ext_sp_hash_nnps = nnps.ExtendedSpatialHashNNPS(dim=3,
particles=[pa],
radius_scale=1.0)
self.strat_radius_nnps = nnps.StratifiedHashNNPS(dim=3,
particles=[pa],
radius_scale=1.0)
# these are the expected cells
self.expected_cells = {
IntPoint(-2, 0, 0): [0, 6],
IntPoint(0, -1, 0): [1, 8],
IntPoint(1, -2, 1): [2],
IntPoint(0, 1, -1): [3, 7, 9],
IntPoint(-1, 0, -2): [4, 5]
}
def create_particles(self):
x, y = np.mgrid[dx:l_tunnel + dx / 4:dx, dx / 2:w_tunnel - dx / 4:dx]
x, y = (np.ravel(t) for t in (x, y))
one = np.ones_like(x)
volume = dx * dx * one
m = volume * rho
fluid = get_particle_array(name='fluid',
m=m,
x=x,
y=y,
h=h0 * one,
V=1.0 / volume,
u=umax * one)
xc, yc = center
cond = (x - xc)**2 + (y - yc)**2 < 1.0
one = np.ones_like(x[cond])
volume = dx * dx * one
solid = get_particle_array(name='solid',
x=x[cond].ravel(),
y=y[cond].ravel(),
m=volume * rho,
rho=one * rho,
h=h0 * one,
V=1.0 / volume)
G.remove_overlap_particles(fluid, solid, dx, dim=2)
x, y = np.mgrid[dx:n_outlet * dx:dx, dx / 2:w_tunnel - dx / 4:dx]
x, y = (np.ravel(t) for t in (x, y))
x += l_tunnel
one = np.ones_like(x)
volume = dx * dx * one
m = volume * rho
outlet = get_particle_array(name='outlet',
x=x,
y=y,
m=m,
h=h0 * one,
V=1.0 / volume,
u=umax * one)
# Setup the inlet particle array with just the particles we need at the
# exit plane which is replicated by the inlet.
y = np.arange(dx / 2, w_tunnel - dx / 4.0, dx)
x = np.zeros_like(y)
one = np.ones_like(x)
volume = one * dx * dx
inlet = get_particle_array(name='inlet',
x=x,
y=y,
m=volume * rho,
h=h0 * one,
u=umax * one,
rho=rho * one,
V=1.0 / volume)
self.scheme.setup_properties([fluid, inlet, outlet, solid])
for p in fluid.properties:
if p not in outlet.properties:
outlet.add_property(p)
return [fluid, solid, inlet, outlet]
def create_particles(self):
# create the particles
dx = self.dx
_x = np.arange(dx / 2, L, dx)
x, y = np.meshgrid(_x, _x)
x = x.ravel()
y = y.ravel()
if self.options.init is not None:
fname = self.options.init
from pysph.solver.utils import load
data = load(fname)
_f = data['arrays']['fluid']
x, y = _f.x.copy(), _f.y.copy()
if self.options.perturb > 0:
np.random.seed(1)
factor = dx * self.options.perturb
x += np.random.random(x.shape) * factor
y += np.random.random(x.shape) * factor
h = np.ones_like(x) * dx
# create the arrays
fluid = get_particle_array(name='fluid', x=x, y=y, h=h)
self.scheme.setup_properties([fluid])
# add the requisite arrays
fluid.add_property('color')
fluid.add_output_arrays(['color'])
print("Taylor green vortex problem :: nfluid = %d, dt = %g" % (
fluid.get_number_of_particles(), self.dt))
# setup the particle properties
pi = np.pi
cos = np.cos
sin = np.sin
# color
fluid.color[:] = cos(2 * pi * x) * cos(4 * pi * y)
# velocities
fluid.u[:] = -U * cos(2 * pi * x) * sin(2 * pi * y)
fluid.v[:] = +U * sin(2 * pi * x) * cos(2 * pi * y)
fluid.p[:] = -U * U * (np.cos(4 * np.pi * x) +
np.cos(4 * np.pi * y)) * 0.25
# mass is set to get the reference density of each phase
fluid.rho[:] = rho0
fluid.m[:] = self.volume * fluid.rho
# volume is set as dx^2
if self.options.scheme == 'tvf':
fluid.V[:] = 1. / self.volume
if self.options.scheme == 'iisph':
# These are needed to update the ghost particle properties.
nfp = fluid.get_number_of_particles()
fluid.orig_idx[:] = np.arange(nfp)
fluid.add_output_arrays(['orig_idx'])
# smoothing lengths
fluid.h[:] = self.hdx * dx
corr = self.kernel_correction
if corr in ['mixed', 'crksph']:
fluid.add_property('cwij')
if corr == 'mixed' or corr == 'gradient':
fluid.add_property('m_mat', stride=9)
fluid.add_property('dw_gamma', stride=3)
elif corr == 'crksph':
fluid.add_property('ai')
fluid.add_property('gradbi', stride=4)
for prop in ['gradai', 'bi']:
fluid.add_property(prop, stride=2)
# return the particle list
return [fluid]
def create_particles(self):
x, y = numpy.mgrid[dxb2:domain_width:dx, dyb2:domain_height:dy]
x = x.ravel()
y = y.ravel()
m = numpy.ones_like(x) * volume * rho0
rho = numpy.ones_like(x) * rho0
h = numpy.ones_like(x) * h0
cs = numpy.ones_like(x) * c0
# additional properties required for the fluid.
additional_props = [
# volume inverse or number density
'V',
# color and gradients
'color',
'scolor',
'cx',
'cy',
'cz',
'cx2',
'cy2',
'cz2',
# discretized interface normals and dirac delta
'nx',
'ny',
'nz',
'ddelta',
# interface curvature
'kappa',
# transport velocities
'uhat',
'vhat',
'what',
'auhat',
'avhat',
'awhat',
# imposed accelerations on the solid wall
'ax',
'ay',
'az',
'wij',
# velocity of magnitude squared
'vmag2',
# variable to indicate reliable normals and normalizing
# constant
'N',
'wij_sum',
]
# get the fluid particle array
fluid = get_particle_array(name='fluid',
x=x,
y=y,
h=h,
m=m,
rho=rho,
cs=cs,
additional_props=additional_props)
# set the color of the inner square
for i in range(x.size):
if ((fluid.x[i] > 0.35) and (fluid.x[i] < 0.65)):
if ((fluid.y[i] > 0.35) and (fluid.y[i] < 0.65)):
fluid.color[i] = 1.0
# particle volume
fluid.V[:] = 1. / volume
# set additional output arrays for the fluid
fluid.add_output_arrays([
'V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta', 'kappa', 'N',
'scolor', 'p'
])
print("2D Square droplet deformation with %d fluid particles" %
(fluid.get_number_of_particles()))
return [
fluid,
]
def create_particles(staggered=True, **kwargs):
if staggered:
x1, y1 = numpy.mgrid[ dxb2:domain_width:dx, dyb2:domain_height:dy ]
x2, y2 = numpy.mgrid[ dx:domain_width:dx, dy:domain_height:dy ]
x1 = x1.ravel(); y1 = y1.ravel()
x2 = x2.ravel(); y2 = y2.ravel()
x = numpy.concatenate( [x1, x2] )
y = numpy.concatenate( [y1, y2] )
volume = dx*dx/2
else:
x, y = numpy.mgrid[ dxb2:domain_width:dx, dyb2:domain_height:dy ]
x = x.ravel(); y = y.ravel()
volume = dx*dx
m = numpy.ones_like(x) * volume * rho0
rho = numpy.ones_like(x) * rho0
h = numpy.ones_like(x) * h0
cs = numpy.ones_like(x) * c0
# additional properties required for the fluid.
additional_props = [
# volume inverse or number density
'V',
# color and gradients
'color', 'scolor', 'cx', 'cy', 'cz', 'cx2', 'cy2', 'cz2',
# discretized interface normals and dirac delta
'nx', 'ny', 'nz', 'ddelta',
# interface curvature
'kappa',
# filtered velocities
'uf', 'vf', 'wf',
# transport velocities
'uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat',
# imposed accelerations on the solid wall
'ax', 'ay', 'az', 'wij',
# velocity of magnitude squared needed for TVF
'vmag2',
# variable to indicate reliable normals and normalizing
# constant
'N', 'wij_sum'
]
# get the fluid particle array
fluid = get_particle_array(
name='fluid', x=x, y=y, h=h, m=m, rho=rho, cs=cs,
additional_props=additional_props)
# set the color of the inner circle
for i in range(x.size):
if ( ((fluid.x[i]-0.5)**2 + (fluid.y[i]-0.5)**2) <= 0.25**2 ):
fluid.color[i] = 1.0
# particle volume
fluid.V[:] = 1./volume
# set additional output arrays for the fluid
fluid.add_output_arrays(['V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta', 'p',
'kappa', 'N', 'scolor'])
print "2D Circular droplet deformation with %d fluid particles"%(
fluid.get_number_of_particles())
return [fluid,]
def get_plate_particles():
x, y, z = numpy.mgrid[0:0.002 + dx:dx, -0.02:0.02 + dx:dx,
-0.02:0.02 + dx:dx]
x = x.ravel()
y = y.ravel()
z = z.ravel()
d = (z * z + y * y)
keep = numpy.flatnonzero(d <= 0.02 * 0.02)
x = x[keep]
y = y[keep]
z = z[keep]
print('%d Target particles' % len(x))
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * dz * ro1
rhof = numpy.ones_like(x) * ro1
csf = numpy.ones_like(x) * cs1
pa = plate = get_particle_array(name="plate",
x=x,
y=y,
z=z,
h=hf,
m=mf,
rho=rhof,
cs=csf)
# add requisite properties
# sound speed etc.
add_properties(pa, 'e')
# velocity gradient properties
add_properties(pa, 'v00', 'v01', 'v02', 'v10', 'v11', 'v12', 'v20', 'v21',
'v22')
# artificial stress properties
add_properties(pa, 'r00', 'r01', 'r02', 'r11', 'r12', 'r22')
# deviatoric stress components
add_properties(pa, 's00', 's01', 's02', 's11', 's12', 's22')
# deviatoric stress accelerations
add_properties(pa, 'as00', 'as01', 'as02', 'as11', 'as12', 'as22')
# deviatoric stress initial values
add_properties(pa, 's000', 's010', 's020', 's110', 's120', 's220')
# standard acceleration variables
add_properties(pa, 'arho', 'au', 'av', 'aw', 'ax', 'ay', 'az', 'ae')
# initial values
add_properties(pa, 'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'e0')
# load balancing properties
pa.set_lb_props(list(pa.properties.keys()))
# removed S_00 and similar components
plate.v[:] = 0.0
return plate
def create_particles(**kwargs):
x, y = numpy.mgrid[ dxb2:domain_width:dx, dyb2:domain_height:dy ]
x = x.ravel(); y = y.ravel()
m = numpy.ones_like(x) * volume * rho0
rho = numpy.ones_like(x) * rho0
h = numpy.ones_like(x) * h0
cs = numpy.ones_like(x) * c0
# additional properties required for the fluid.
additional_props = [
# volume inverse or number density
'V',
# color and gradients
'color', 'scolor', 'cx', 'cy', 'cz', 'cx2', 'cy2', 'cz2',
# discretized interface normals and dirac delta
'nx', 'ny', 'nz', 'ddelta',
# interface curvature
'kappa',
# filtered velocities
'uf', 'vf', 'wf',
# transport velocities
'uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat',
# imposed accelerations on the solid wall
'ax', 'ay', 'az', 'wij',
# velocity of magnitude squared
'vmag2',
# variable to indicate reliable normals and normalizing
# constant
'N', 'wij_sum',
]
# get the fluid particle array
fluid = get_particle_array(
name='fluid', x=x, y=y, h=h, m=m, rho=rho, cs=cs,
additional_props=additional_props)
# set the color of the inner square
for i in range(x.size):
if ( (fluid.x[i] > 0.35) and (fluid.x[i] < 0.65) ):
if ( (fluid.y[i] > 0.35) and (fluid.y[i] < 0.65) ):
fluid.color[i] = 1.0
# particle volume
fluid.V[:] = 1./volume
# set additional output arrays for the fluid
fluid.add_output_arrays(['V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta',
'kappa', 'N', 'scolor', 'p'])
print "2D Square droplet deformation with %d fluid particles"%(
fluid.get_number_of_particles())
return [fluid,]
def setup(self):
self.pa = get_particle_array(name='f', x=[0.0, 1.0], m=1.0, rho=2.0)
def create_particles(self):
#x,y = numpy.mgrid[-1.05:1.05+1e-4:dx, -0.105:0.105+1e-4:dx]
spacing = 0.041 # spacing = 2*5cm
x, y = numpy.mgrid[-ro:ro:dx, -ro:ro:dx]
x = x.ravel()
y = y.ravel()
d = (x * x + y * y)
keep = numpy.flatnonzero((ri * ri <= d) * (d < ro * ro))
x = x[keep]
y = y[keep]
x = numpy.concatenate([x - spacing, x + spacing])
y = numpy.concatenate([y, y])
print('Ellastic Collision with %d particles' % (x.size))
print(
"Shear modulus G = %g, Young's modulus = %g, Poisson's ratio =%g" %
(G, E, nu))
#print bdry, numpy.flatnonzero(bdry)
m = numpy.ones_like(x) * dx * dx
h = numpy.ones_like(x) * hdx * dx
rho = numpy.ones_like(x)
z = numpy.zeros_like(x)
p = 0.5 * 1.0 * 100 * 100 * (1 - (x**2 + y**2))
cs = numpy.ones_like(x) * 10000.0
# u is set later
v = z
u_f = 0.059
p *= 0
h *= 1
# create the particle array
pa = get_particle_array(name="solid",
x=x + spacing,
y=y,
m=m,
rho=rho,
h=h,
p=p,
cs=cs,
u=z,
v=v)
pa.cs[:] = c0
pa.u = pa.cs * u_f * (2 * (x < 0) - 1)
# add requisite properties
# sound speed etc.
add_properties(pa, 'cs', 'e')
# velocity gradient properties
add_properties(pa, 'v00', 'v01', 'v02', 'v10', 'v11', 'v12', 'v20',
'v21', 'v22')
# artificial stress properties
add_properties(pa, 'r00', 'r01', 'r02', 'r11', 'r12', 'r22')
# deviatoric stress components
add_properties(pa, 's00', 's01', 's02', 's11', 's12', 's22')
# deviatoric stress accelerations
add_properties(pa, 'as00', 'as01', 'as02', 'as11', 'as12', 'as22')
# deviatoric stress initial values
add_properties(pa, 's000', 's010', 's020', 's110', 's120', 's220')
# standard acceleration variables
add_properties(pa, 'arho', 'au', 'av', 'aw', 'ax', 'ay', 'az', 'ae')
# initial values
add_properties(pa, 'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'e0')
# load balancing properties
pa.set_lb_props(list(pa.properties.keys()))
return [
pa,
]
def create_particles(self):
fluid_x, fluid_y = get_2d_block(dx=dx,
length=Lx,
height=Ly,
center=np.array([0., 0.]))
rho_fluid = np.ones_like(fluid_x) * rho0
m_fluid = rho_fluid * volume
h_fluid = np.ones_like(fluid_x) * h0
cs_fluid = np.ones_like(fluid_x) * c0
wall_x, wall_y = get_2d_block(dx=dx,
length=Lx + 6 * dx,
height=Ly + 6 * dx,
center=np.array([0., 0.]))
rho_wall = np.ones_like(wall_x) * rho0
m_wall = rho_wall * volume
h_wall = np.ones_like(wall_x) * h0
cs_wall = np.ones_like(wall_x) * c0
additional_props = [
'V', 'color', 'scolor', 'cx', 'cy', 'cz', 'cx2', 'cy2', 'cz2',
'nx', 'ny', 'nz', 'ddelta', 'uhat', 'vhat', 'what', 'auhat',
'avhat', 'awhat', 'ax', 'ay', 'az', 'wij', 'vmag2', 'N', 'wij_sum',
'rho0', 'u0', 'v0', 'w0', 'x0', 'y0', 'z0', 'kappa', 'arho', 'nu',
'wg', 'ug', 'vg', 'pi00', 'pi01', 'pi10', 'pi11', 'alpha'
]
consts = {'max_ddelta': np.zeros(1, dtype=float)}
fluid = get_particle_array(name='fluid',
x=fluid_x,
y=fluid_y,
h=h_fluid,
m=m_fluid,
rho=rho_fluid,
cs=cs_fluid,
additional_props=additional_props,
constants=consts)
for i in range(len(fluid.x)):
if (fluid.x[i] * fluid.x[i] +
fluid.y[i] * fluid.y[i]) < 0.1875 * 0.1875:
fluid.color[i] = 1.0
else:
fluid.color[i] = 0.0
fluid.alpha[:] = sigma
wall = get_particle_array(name='wall',
x=wall_x,
y=wall_y,
h=h_wall,
m=m_wall,
rho=rho_wall,
cs=cs_wall,
additional_props=additional_props)
wall.color[:] = 0.0
remove_overlap_particles(wall, fluid, dx_solid=dx, dim=2)
fluid.add_output_arrays([
'V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta', 'kappa', 'N',
'scolor', 'p'
])
wall.add_output_arrays([
'V', 'color', 'cx', 'cy', 'nx', 'ny', 'ddelta', 'kappa', 'N',
'scolor', 'p'
])
for i in range(len(fluid.x)):
R = sqrt(
r(fluid.x[i], fluid.y[i]) + 0.0001 * fluid.h[i] * fluid.h[i])
fluid.u[i] = v0 * fluid.x[i] * (1.0 - (fluid.y[i] * fluid.y[i]) /
(r0 * R)) * np.exp(-R / r0) / r0
fluid.v[i] = -v0 * fluid.y[i] * (1.0 - (fluid.x[i] * fluid.x[i]) /
(r0 * R)) * np.exp(-R / r0) / r0
fluid.nu[:] = nu
return [fluid, wall]
def create_particles(self):
_x = np.arange(dx / 2, Lx, dx)
# create the fluid particles
_y = np.arange(dx / 2, Ly, dx)
x, y = np.meshgrid(_x, _y)
fx = x.ravel()
fy = y.ravel()
# create the channel particles at the top
_y = np.arange(Ly + dx / 2, Ly + dx / 2 + ghost_extent, dx)
x, y = np.meshgrid(_x, _y)
tx = x.ravel()
ty = y.ravel()
# create the channel particles at the bottom
_y = np.arange(-dx / 2, -dx / 2 - ghost_extent, -dx)
x, y = np.meshgrid(_x, _y)
bx = x.ravel()
by = y.ravel()
# concatenate the top and bottom arrays
cx = np.concatenate((tx, bx))
cy = np.concatenate((ty, by))
# create the arrays
channel = get_particle_array(name="channel",
x=cx,
y=cy,
rho=rho0 * np.ones_like(cx))
fluid = get_particle_array(name="fluid",
x=fx,
y=fy,
rho=rho0 * np.ones_like(fx))
print("Couette flow :: Re = %g, nfluid = %d, nchannel=%d, dt = %g" % (
Re,
fluid.get_number_of_particles(),
channel.get_number_of_particles(),
dt,
))
self.scheme.setup_properties([fluid, channel])
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of rho0
fluid.m[:] = volume * rho0
channel.m[:] = volume * rho0
# volume is set as dx^2
fluid.V[:] = 1.0 / volume
channel.V[:] = 1.0 / volume
# smoothing lengths
fluid.h[:] = hdx * dx
channel.h[:] = hdx * dx
# channel velocity on upper portion
indices = np.where(channel.y > d)[0]
channel.u[indices] = Vmax
# return the particle list
return [fluid, channel]
if __name__ == '__main__':
all_functions = inspect.getmembers(dataset, inspect.isfunction)
i = 0
for name, test in all_functions:
print i, name
i+=1
choices = raw_input("Choose tests: ")
N_s = raw_input("Enter N in that order: ")
choices = [int(i) for i in choices.split()]
N_s = [int(i) for i in N_s.split()]
pa_list = []
H = float(sys.argv[1])
for test, N in zip(choices, N_s):
x, y, z, h = make_testcase(all_functions[test][1], N)
pa = get_particle_array(x=x, y=y, z=z, h=h)
pa_list.append(pa)
columns = [all_functions[i][0] for i in choices]
index = ["LinkedListNNPS", "SpatialHashNNPS", "ExtendedSpatialHash(approx)",
"ExtendedSpatialHash(exact)"]
value = [[],[],[],[]]
nbrs = UIntArray()
for i,pa in enumerate(pa_list):
value[0].append(time_pysph_nnps(pa, nbrs, pa.get_number_of_particles()))
value[1].append(time_spatial_hash_nnps(pa, nbrs, pa.get_number_of_particles()))
value[2].append(time_extended_spatial_hash(pa,nbrs,H,True,pa.get_number_of_particles()))
value[3].append(time_extended_spatial_hash(pa,nbrs,H,False,pa.get_number_of_particles()))
if len(value[0]) == 1:
value = [value[0], value[1], value[2], value[3]]
print pd.DataFrame(value, index, columns)
def create_particles(**kwargs):
# create all the particles
_x = np.arange( dx/2, L, dx )
_y = np.arange( dx/2, H, dx)
x, y = np.meshgrid(_x, _y); x = x.ravel(); y = y.ravel()
# sort out the fluid and the solid
indices = []
cx = 0.5 * L; cy = 0.5 * H
for i in range(x.size):
xi = x[i]; yi = y[i]
if ( np.sqrt( (xi-cx)**2 + (yi-cy)**2 ) > a ):
#if ( (yi > 0) and (yi < H) ):
indices.append(i)
# create the arrays
solid = get_particle_array(name='solid', x=x, y=y)
# remove the fluid particles from the solid
fluid = solid.extract_particles(indices); fluid.set_name('fluid')
solid.remove_particles(indices)
print "Periodic cylinders :: Re = %g, nfluid = %d, nsolid=%d, dt = %g"%(
Re, fluid.get_number_of_particles(),
solid.get_number_of_particles(), dt)
# add requisite properties to the arrays:
# particle volume
fluid.add_property('V')
solid.add_property('V' )
# advection velocities and accelerations
for name in ('uhat', 'vhat', 'what', 'auhat', 'avhat', 'awhat', 'au', 'av', 'aw'):
fluid.add_property(name)
# kernel summation correction for the solid
solid.add_property('wij')
# imposed accelerations on the solid
solid.add_property('ax')
solid.add_property('ay')
solid.add_property('az')
# Shepard filtered velocities for the fluid
for name in ['uf', 'vf', 'wf']:
fluid.add_property(name)
# magnitude of velocity
fluid.add_property('vmag')
# density acceleration
fluid.add_property('arho')
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of rho0
fluid.m[:] = volume * rho0
solid.m[:] = volume * rho0
solid.rho[:] = rho0
# reference pressures and densities
fluid.rho[:] = rho0
# volume is set as dx^2
fluid.V[:] = 1./volume
solid.V[:] = 1./volume
# smoothing lengths
fluid.h[:] = hdx * dx
solid.h[:] = hdx * dx
# return the particle list
return [fluid, solid]
def create_particles(self):
'''
Set up particle arrays
'''
dx = self.dx
L = self.L
_x = np.arange(dx / 2, L, dx)
x, y = np.meshgrid(_x, _x)
if self.options.perturb > 0:
np.random.seed(1)
factor = dx * self.options.perturb
x += np.random.random(x.shape) * factor
y += np.random.random(x.shape) * factor
m = self.volume * self.rho0
h = self.hdx * dx
re = self.options.re
b = -8.0 * pi * pi / re
u0, v0, p0 = exact_solution(U=self.U, b=b, t=0, x=x, y=y)
color0 = cos(2 * pi * x) * cos(4 * pi * y)
# create the arrays
fluid = get_particle_array(name='fluid',
x=x,
y=y,
m=m,
h=h,
u=u0,
v=v0,
rho=self.rho0,
p=p0,
color=color0)
fluid.add_property('m_mat', stride=9)
fluid.add_property('gradrho', stride=3)
fluid.add_property('gradlmda', stride=3)
add_props = [
'lmda',
'rho0',
'u0',
'v0',
'w0',
'x0',
'y0',
'z0',
'ax',
'ay',
'az',
'DRh',
'DY',
'DX',
'DZ',
'uhat',
'vhat',
'what',
'auhat',
'avhat',
'awhat',
'vmag2',
'V',
'arho',
'vmag',
]
for i in add_props:
fluid.add_property(i)
rk4_props = [
'rho',
'ax',
'ay',
'au',
'av',
'arho',
'x0',
'y0',
'u0',
'v0',
'rho0',
'xstar',
'ystar',
'ustar',
'vstar',
'rhostar',
'vmag',
'vmag2',
]
for i in rk4_props:
fluid.add_property(i)
fluid.set_output_arrays([
'x', 'y', 'z', 'u', 'v', 'w', 'rho', 'p', 'h', 'm', 'vmag',
'vmag2', 'pid', 'gid', 'tag', 'DRh', 'lmda'
])
# setup the particle properties
volume = dx * dx
# mass is set to get the reference density of rho0
fluid.V[:] = 1. / volume
fluid.lmda[:] = 1.0
return [fluid]
# for the hexagonal particle spacing, dx*dy is only an approximate
# expression for the particle volume. As far as the summation density
# test is concerned, the value will be uniform but not equal to 1. To
# reproduce a density profile of 1, we need to estimate the kernel sum
# or number density of the distribution based on the kernel
wij_sum_estimate = get_number_density_hcp(dx, dy, k, h0)
volume = 1./wij_sum_estimate
print 'Volume estimates :: dx^2 = %g, Number density = %g'%(dx*dy, volume)
x = x.ravel(); y = y.ravel()
h = numpy.ones_like(x) * h0
m = numpy.ones_like(x) * volume
wij = numpy.zeros_like(x)
# 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=min, xmax=max, ymin=min, ymax=max,periodic_in_x=False, periodic_in_y=False)
print "NumPa:", pa.num_real_particles
# NNPS object for nearest neighbor queries
nps = BoxSortNNPS(dim=2, particles=[pa,], radius_scale=k.radius_scale, domain=domain)
#nps.bin()
DMP = DeviceMemoryPool()
"""
cells = nps.cells
max_cell_pop = 0
def create_particles(self):
nx, ny, nz = 10, 10, 10
dx = self.dx
# x, y, z = create_an_unsymmetric_body(self.dx * 2., 0.5, 0.5, 1.)
x, y, z = np.mgrid[0:1:nx * 1j, 0:1:ny * 1j, 0:1:nz * 1j]
x = x.flat
y = y.flat
z = z.flat
m = np.ones_like(x) * dx * dx * dx * self.rho0
h = np.ones_like(x) * self.hdx * dx
# radius of each sphere constituting in cube
rad_s = np.ones_like(x) * dx / 4.
body = get_particle_array(name='body', x=x, y=y, z=z, h=h, m=m)
body_id = np.zeros(len(x), dtype=int)
body.add_property('body_id', type='int')
body.body_id[:] = body_id[:]
setup_rigid_body_unconstrained_dynamics(body, principal_moi=False)
setup_rigid_body_collision_dynamics(body, rad_s)
add_properties(body, 'tang_velocity_z', 'tang_disp_y',
'tang_velocity_x', 'tang_disp_x', 'tang_velocity_y',
'tang_disp_z')
# body.vc[0] = 0.5
# body.vc[1] = 0.5
body.omega[1] = 5.
# this must run
set_angular_momentum([body])
# create child body
x, y, z = np.mgrid[-2. * dx:1 + 2. * dx:nx * 1j,
-2. * dx:1 + 2. * dx:ny * 1j,
-2. * dx:1 + 2. * dx:nz * 1j]
x = x.flat
y = y.flat
z = z.flat
rad_s = np.ones_like(x) * dx / 4.
child_body = get_particle_array(name='child_body',
x=x,
y=y,
z=z,
h=h,
m=m)
body_id = np.zeros(len(x), dtype=int)
child_body.add_property('body_id', type='int')
child_body.body_id[:] = body_id[:]
setup_child_body(child_body, body)
setup_rigid_body_collision_dynamics(child_body, rad_s)
add_properties(child_body, 'tang_velocity_z', 'tang_disp_y',
'tang_velocity_x', 'tang_disp_x', 'tang_velocity_y',
'tang_disp_z')
# from IPython import embed; embed()
return [body, child_body]