"""Basic equations for Gas-dynamics"""
from pysph.sph.equation import Equation
from math import sqrt
[docs]class ScaleSmoothingLength(Equation):
def __init__(self, dest, sources=None, factor=2.0):
super(ScaleSmoothingLength, self).__init__(dest, sources)
self.factor = factor
[docs] def loop(self, d_idx, d_h):
d_h[d_idx] = d_h[d_idx] * self.factor
[docs]class UpdateSmoothingLengthFromVolume(Equation):
def __init__(self, dest, sources=None, k=1.2, dim=1.0):
super(UpdateSmoothingLengthFromVolume, self).__init__(dest, sources)
self.k = k
self.dim1 = 1./dim
[docs] def loop(self, d_idx, d_m, d_rho, d_h):
d_h[d_idx] = self.k * pow( d_m[d_idx]/d_rho[d_idx], self.dim1)
[docs]class SummationDensity(Equation):
def __init__(
self, dest, sources=None, dim=2,
density_iterations=False, iterate_only_once=False, k=1.2, htol=1e-6):
r"""Summation density with iterative solution of the smoothing lengths.
Parameters:
density_iterations : bint
Flag to indicate density iterations are required.
iterate_only_once : bint
Flag to indicate if only one iteration is required
k : double
Kernel scaling factor
htol : double
Iteration tolerance
"""
self.density_iterations = density_iterations
self.iterate_only_once = iterate_only_once
self.dim = dim
self.k = k
self.htol = htol
# by default, we set the equation_has_converged attribute to True. If
# density_iterations is set to True, we will have at least one
# iteration to determine the new smoothing lengths since the
# 'converged' property of the particles is intialized to False
self.equation_has_converged = 1
super(SummationDensity, self).__init__(dest, sources)
[docs] def initialize(
self, d_idx, d_rho, d_div, d_grhox, d_grhoy, d_arho, d_dwdh):
d_rho[d_idx] = 0.0
d_div[d_idx] = 0.0
d_grhox[d_idx] = 0.0
d_grhoy[d_idx] = 0.0
d_arho[d_idx] = 0.0
d_dwdh[d_idx] = 0.0
# set the converged attribute for the Equation to True. Within
# the post-loop, if any particle hasn't converged, this is set
# to False. The Group can therefore iterate till convergence.
self.equation_has_converged = 1
[docs] def loop(self, d_idx, s_idx, d_rho, d_grhox, d_grhoy, d_arho,
d_dwdh, s_m, d_converged, VIJ, WI, DWI, GHI):
mj = s_m[s_idx]
vijdotdwij = VIJ[0]*DWI[0] + VIJ[1]*DWI[1] + VIJ[2]*DWI[2]
# density
d_rho[d_idx] += mj * WI
# density accelerations
d_arho[d_idx] += mj * vijdotdwij
# gradient of density
d_grhox[d_idx] += mj * DWI[0]
d_grhoy[d_idx] += mj * DWI[1]
# gradient of kernel w.r.t h
d_dwdh[d_idx] += mj * GHI
[docs] def post_loop(self, d_idx, d_arho, d_rho, d_div, d_omega, d_dwdh,
d_h0, d_h, d_m, d_ah, d_converged):
# iteratively find smoothing length consistent with the
if self.density_iterations:
if not ( d_converged[d_idx] == 1 ):
# current mass and smoothing length. The initial
# smoothing length h0 for this particle must be set
# outside the Group (that is, in the integrator)
mi = d_m[d_idx]; hi = d_h[d_idx]; hi0 = d_h0[d_idx]
# density from the mass, smoothing length and kernel
# scale factor
rhoi = mi/(hi/self.k)**self.dim
dhdrhoi = -hi/( self.dim*d_rho[d_idx] )
dwdhi = d_dwdh[d_idx]
omegai = 1.0 - dhdrhoi*dwdhi
# correct omegai
if omegai < 0:
omegai = 1.0
# kernel multiplier. These are the multiplicative
# pre-factors, or the "grah-h" terms in the
# equations. Remember that the equations use 1/omega
gradhi = 1.0/omegai
d_omega[d_idx] = gradhi
# the non-linear function and it's derivative
func = rhoi - d_rho[d_idx]
dfdh = omegai/dhdrhoi
# Newton Raphson estimate for the new h
hnew = hi - func/dfdh
# Nanny control for h
if ( hnew > 1.2 * hi ):
hnew = 1.2 * hi
elif ( hnew < 0.8 * hi ):
hnew = 0.8 * hi
# overwrite if gone awry
if ( (hnew <= 1e-6) or (gradhi < 1e-6) ):
hnew = self.k * (mi/d_rho[d_idx])**(1./self.dim)
# check for convergence
diff = abs( hnew-hi )/hi0
if not ( (diff < self.htol) and (omegai > 0) or self.iterate_only_once):
# this particle hasn't converged. This means the
# entire group must be repeated until this fellow
# has converged, or till the maximum iteration has
# been reached.
self.equation_has_converged = -1
# set particle properties for the next
# iteration. For the 'converged' array, a value of
# 0 indicates the particle hasn't converged
d_h[d_idx] = hnew
d_converged[d_idx] = 0
else:
d_arho[d_idx] *= d_omega[d_idx]
d_ah[d_idx] = d_arho[d_idx] * dhdrhoi
d_converged[d_idx] = 1
# comptue the divergence of velocity
d_div[d_idx] = -d_arho[d_idx]/d_rho[d_idx]
[docs] def converged(self):
return self.equation_has_converged
[docs]class IdealGasEOS(Equation):
def __init__(self, dest, sources=None, gamma=1.4):
self.gamma = gamma
self.gamma1 = gamma - 1.0
super(IdealGasEOS, self).__init__(dest, sources)
[docs] def loop(self, d_idx, d_p, d_rho, d_e, d_cs):
d_p[d_idx] = self.gamma1 * d_rho[d_idx] * d_e[d_idx]
d_cs[d_idx] = sqrt( self.gamma * d_p[d_idx]/d_rho[d_idx] )
[docs]class Monaghan92Accelerations(Equation):
def __init__(self, dest, sources, alpha=1.0, beta=2.0):
self.alpha = alpha
self.beta = beta
super(Monaghan92Accelerations, self).__init__(dest, sources)
[docs] def initialize(self, d_idx, d_au, d_av, d_aw, d_ae):
d_au[d_idx] = 0.0
d_av[d_idx] = 0.0
d_aw[d_idx] = 0.0
d_ae[d_idx] = 0.0
[docs] def loop(self, d_idx, s_idx, d_rho, s_rho, d_p, s_p, d_cs, s_cs,
d_au, d_av, d_aw, d_ae, s_m,
VIJ, DWIJ, XIJ, EPS, HIJ, R2IJ, RHOIJ1):
rhoi2 = d_rho[d_idx] * d_rho[d_idx]
rhoj2 = s_rho[s_idx] * s_rho[s_idx]
tmpi = d_p[d_idx]/rhoi2
tmpj = s_p[s_idx]/rhoj2
vijdotxij = VIJ[0]*XIJ[0] + VIJ[1]*XIJ[1] + VIJ[2]*XIJ[2]
piij = 0.0
if vijdotxij < 0:
muij = HIJ*vijdotxij/(R2IJ + EPS)
cij = 0.5 * (d_cs[d_idx] + s_cs[s_idx])
piij = -self.alpha*cij*muij + self.beta*muij*muij
piij *= RHOIJ1
d_au[d_idx] += -s_m[s_idx] * (tmpi + tmpj + piij) * DWIJ[0]
d_av[d_idx] += -s_m[s_idx] * (tmpi + tmpj + piij) * DWIJ[1]
d_aw[d_idx] += -s_m[s_idx] * (tmpi + tmpj + piij) * DWIJ[2]
vijdotdwij = VIJ[0]*DWIJ[0] + VIJ[1]*DWIJ[1] + VIJ[2]*DWIJ[2]
d_ae[d_idx] += 0.5 * s_m[s_idx] * (tmpi + tmpj + piij) * vijdotdwij
[docs]class MPMAccelerations(Equation):
def __init__(
self, dest, sources, beta=2.0,
update_alapha1=False, update_alapha2=False,
alpha1_min=0.1, alpha2_min=0.1, sigma=0.1):
self.beta = beta
self.sigma = sigma
self.update_alapha1 = update_alapha1
self.update_alapha2 = update_alapha2
self.alpha1_min = alpha1_min
self.alpha2_min = alpha2_min
super(MPMAccelerations, self).__init__(dest, sources)
[docs] def initialize(self, d_idx, d_au, d_av, d_aw, d_ae, d_am,
d_aalpha1, d_aalpha2, d_del2e):
d_au[d_idx] = 0.0
d_av[d_idx] = 0.0
d_aw[d_idx] = 0.0
d_ae[d_idx] = 0.0
d_aalpha1[d_idx] = 0.0
d_aalpha2[d_idx] = 0.0
d_del2e[d_idx] = 0.0
[docs] def loop(self, d_idx, s_idx, d_m, s_m, d_p, s_p, d_cs, s_cs,
d_e, s_e, d_rho, s_rho, d_au, d_av, d_aw, d_ae,
d_omega, s_omega, XIJ, VIJ, DWI, DWJ, DWIJ, HIJ,
d_del2e, d_alpha1, s_alpha1, d_alpha2, s_alpha2,
EPS, RIJ, R2IJ, RHOIJ, DT_ADAPT):
# particle pressure
pi = d_p[d_idx]
pj = s_p[s_idx]
# pi/rhoi**2
rhoi2 = d_rho[d_idx]*d_rho[d_idx]
pibrhoi2 = pi/rhoi2
# pj/rhoj**2
rhoj2 = s_rho[s_idx]*s_rho[s_idx]
pjbrhoj2 = pj/rhoj2
# averaged sound speed
cij = 0.5 * (d_cs[d_idx] + s_cs[s_idx])
# averaged mass
mi = d_m[d_idx]
mj = s_m[s_idx]
mij = 0.5 * (mi + mj)
# averaged sound speed
ci = d_cs[d_idx]
cj = s_cs[s_idx]
cij = 0.5 * (ci + cj)
# normalized interaction vector
if RIJ < 1e-8:
XIJ[0] = 0.0
XIJ[1] = 0.0
XIJ[2] = 0.0
else:
XIJ[0] /= RIJ
XIJ[1] /= RIJ
XIJ[2] /= RIJ
# v_{ij} \cdot r_{ij} or vijdotxij
dot = VIJ[0]*XIJ[0] + VIJ[1]*XIJ[1] + VIJ[2]*XIJ[2]
# scalar part of the kernel gradient DWIJ
Fij = XIJ[0]*DWIJ[0] + XIJ[1]*DWIJ[1] + XIJ[2]*DWIJ[2]
# signal velocities
pdiff = abs(pi-pj)
vsig1 = 0.5 * max(cij - self.beta*dot, 0.0)
vsig2 = sqrt( pdiff/RHOIJ )
# compute the Courant-limited time step factor.
DT_ADAPT[0] = max( DT_ADAPT[0], cij + self.beta * dot )
# Artificial viscosity
if dot <= 0.0:
# viscosity
alpha1 = 0.5 * (d_alpha1[d_idx] + s_alpha1[s_idx])
tmpv = mj/RHOIJ * alpha1 * vsig1 * dot
d_au[d_idx] += tmpv * DWIJ[0]
d_av[d_idx] += tmpv * DWIJ[1]
d_aw[d_idx] += tmpv * DWIJ[2]
# viscous contribution to the thermal energy
d_ae[d_idx] += -0.5*mj/RHOIJ*alpha1*vsig1*dot*dot*Fij
# grad-h correction terms. These will be set to 1.0 by the
# integrator and thus can be used safely.
omegai = d_omega[d_idx]; omegaj = s_omega[s_idx]
# gradient terms
d_au[d_idx] += -mj*(pibrhoi2*omegai*DWI[0] + pjbrhoj2*omegaj*DWJ[0])
d_av[d_idx] += -mj*(pibrhoi2*omegai*DWI[1] + pjbrhoj2*omegaj*DWJ[1])
d_aw[d_idx] += -mj*(pibrhoi2*omegai*DWI[2] + pjbrhoj2*omegaj*DWJ[2])
# accelerations for the thermal energy
vijdotdwi = VIJ[0]*DWI[0] + VIJ[1]*DWI[1] + VIJ[2]*DWI[2]
d_ae[d_idx] += mj * pibrhoi2 * omegai * vijdotdwi
# thermal conduction
alpha2 = 0.5 * (d_alpha2[d_idx] + s_alpha2[s_idx])
eij = d_e[d_idx] - s_e[s_idx]
d_ae[d_idx] += mj/RHOIJ * alpha2 * vsig2 * eij * Fij
# Laplacian of thermal energy
d_del2e[d_idx] += mj/s_rho[s_idx] * eij/(RIJ + EPS) * Fij
[docs] def post_loop(self, d_idx, d_h, d_cs, d_alpha1, d_aalpha1, d_div,
d_del2e, d_e, d_alpha2, d_aalpha2):
hi = d_h[d_idx]
tau = hi/(self.sigma*d_cs[d_idx])
if self.update_alapha1:
S1 = max( -d_div[d_idx], 0.0 )
d_aalpha1[d_idx] = (self.alpha1_min - d_alpha1[d_idx])/tau + S1
if self.update_alapha2:
#S2 = d_h[d_idx] * abs(d_del2e[d_idx])/sqrt(d_e[d_idx])
S2 = 0.01 * d_h[d_idx] * d_del2e[d_idx]
d_aalpha2[d_idx] = (self.alpha2_min - d_alpha2[d_idx])/tau + S2