def create_particles(self):
data = ud.uniform_distribution_cubic2D(self.dx, self.xmin, self.xmax,
self.ymin, self.ymax)
x = data[0]
y = data[1]
box_indices = numpy.where((x > 0.25) & (x < 0.75) & (y > 0.25)
& (y < 0.75))[0]
rho = numpy.ones_like(x) * self.rho0
rho[box_indices] = self.rhoi
e = self.p / ((self.gamma - 1) * rho)
m = self.dx * self.dx * rho
h = self.hdx * self.dx
fluid = gpa(name='fluid',
x=x,
y=y,
p=self.p,
rho=rho,
e=e,
u=0.,
v=0.,
h=self.hdx * self.dx,
m=m,
h0=h)
self.scheme.setup_properties([fluid])
return [fluid]
def create_particles(self):
hcp = self.options.hcp
# 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 = self._setup_particle_properties([fluid, solid, obstacle],
volume=volume)
return particles
def create_particles(**kwargs):
global dx
data = ud.uniform_distribution_cubic2D(dx, xmin, xmax, ymin, ymax)
x = data[0]; y = data[1]
dx = data[2]; dy = data[3]
# volume estimate
volume = dx*dy
# indices on either side of the initial discontinuity
right_indices = numpy.where( x > 0.0 )[0]
# density is uniform
rho = numpy.ones_like(x)
# pl = 100.0, pr = 0.1
p = numpy.ones_like(x) * 1000.0
p[right_indices] = 0.01
# const h and mass
h = numpy.ones_like(x) * h0
m = numpy.ones_like(x) * volume * rho
# thermal energy from the ideal gas EOS
e = p/(gamma1*rho)
fluid = gpa(name='fluid', x=x, y=y, rho=rho, p=p, e=e, h=h, m=m, h0=h.copy())
print "2D Shocktube with %d particles"%(fluid.get_number_of_particles())
return [fluid,]
def create_particles(self):
data = ud.uniform_distribution_cubic2D(self.dx, xmin, xmax, ymin, ymax)
x = data[0].ravel()
y = data[1].ravel()
y1 = numpy.where((y >= 0) & (y < 0.25))[0]
y2 = numpy.where((y >= 0.25) & (y < 0.5))[0]
y3 = numpy.where((y >= 0.5) & (y < 0.75))[0]
y4 = numpy.where((y >= 0.75) & (y < 1.0))[0]
rho1 = rhoi_1 - rhoi_m * numpy.exp((y[y1] - 0.25) / delta)
rho2 = rhoi_2 + rhoi_m * numpy.exp((0.25 - y[y2]) / delta)
rho3 = rhoi_2 + rhoi_m * numpy.exp((y[y3] - 0.75) / delta)
rho4 = rhoi_1 - rhoi_m * numpy.exp((0.75 - y[y4]) / delta)
u1 = v_i1 - v_im * numpy.exp((y[y1] - 0.25) / delta)
u2 = v_i2 + v_im * numpy.exp((0.25 - y[y2]) / delta)
u3 = v_i2 + v_im * numpy.exp((y[y3] - 0.75) / delta)
u4 = v_i1 - v_im * numpy.exp((0.75 - y[y4]) / delta)
v = dely * numpy.sin(2 * numpy.pi * x / wavelen)
p = 2.5
rho = numpy.concatenate((rho1, rho2, rho3, rho4))
u = numpy.concatenate((u1, u2, u3, u4))
v = numpy.concatenate((v[y1], v[y2], v[y3], v[y4]))
x = numpy.concatenate((x[y1], x[y2], x[y3], x[y4]))
y = numpy.concatenate((y[y1], y[y2], y[y3], y[y4]))
e = p / ((gamma - 1) * rho)
m = self.dx * self.dx * rho
h = self.dx * self.hdx
fluid = gpa(name='fluid',
x=x,
y=y,
u=u,
v=v,
rho=rho,
p=p,
e=e,
m=m,
h=h,
h0=h)
self.scheme.setup_properties([fluid])
return [fluid]
def create_particles(self):
global dx
data = ud.uniform_distribution_cubic2D(dx, xmin, xmax, ymin, ymax)
x = data[0]
y = data[1]
dx = data[2]
dy = data[3]
# volume estimate
volume = dx * dy
# indices on either side of the initial discontinuity
right_indices = numpy.where(x > x0)[0]
# density is uniform
rho = numpy.ones_like(x) * self.rhol
rho[right_indices] = self.rhor
# pl = 100.0, pr = 0.1
p = numpy.ones_like(x) * self.pl
p[right_indices] = self.pr
# const h and mass
h = numpy.ones_like(x) * self.hdx * self.dx
m = numpy.ones_like(x) * volume * rho
# ul = ur = 0
u = numpy.ones_like(x) * self.ul
u[right_indices] = self.ur
# vl = vr = 0
v = numpy.ones_like(x) * self.vl
v[right_indices] = self.vr
# thermal energy from the ideal gas EOS
e = p / (gamma1 * rho)
fluid = gpa(name='fluid',
x=x,
y=y,
rho=rho,
p=p,
e=e,
h=h,
m=m,
h0=h.copy(),
u=u,
v=v)
self.scheme.setup_properties([fluid])
print("2D Shocktube with %d particles" %
(fluid.get_number_of_particles()))
return [
fluid,
]
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 create_particles(self):
global dx
data = ud.uniform_distribution_cubic2D(self.dx, xmin, xmax, ymin, ymax)
x = data[0].ravel()
y = data[1].ravel()
dx = data[2]
volume = dx * dx
rho = 1 + 0.2 * numpy.sin(numpy.pi * (x + y))
p = numpy.ones_like(x) * self.p
# const h and mass
h = numpy.ones_like(x) * self.hdx * dx
m = numpy.ones_like(x) * volume * rho
# u = 1
u = numpy.ones_like(x) * self.u
# v = -1
v = numpy.ones_like(x) * self.v
# thermal energy from the ideal gas EOS
e = p / (gamma1 * rho)
fluid = gpa(name='fluid',
x=x,
y=y,
rho=rho,
p=p,
e=e,
h=h,
m=m,
h0=h.copy(),
u=u,
v=v)
self.scheme.setup_properties([fluid])
print("2D Accuracy Test with %d particles" %
(fluid.get_number_of_particles()))
return [
fluid,
]
dx = 0.001; dxb2 = 0.5 * dx
h0 = 2.*dx
max = 1.
min = 0.
is2D = True
if is2D:
maxz = 0.
minz = 0.
else:
maxz = max
minz = min
# Uniform lattice distribution of particles
x, y, dx, dy, xmin, xmax, ymin, ymax = uniform_distribution_cubic2D(
dx, xmin=min, xmax=max, ymin=min, ymax=max)
# Uniform hexagonal close packing arrangement of particles
#x, y, dx, dy, xmin, xmax, ymin, ymax = uniform_distribution_hcp2D(
# dx, xmin=0.0, xmax=1., ymin=0.0, ymax=1., adjust=True)
# SPH kernel
#k = CubicSpline(dim=2)
#k = Gaussian(dim=2)
#k = QuinticSpline(dim=2)
k = WendlandQuintic(dim=2)
# 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
