def min_noif(a, b):
# Returns the minimum of a and b
if a == sp.sympify(0):
return sp.Rational(1, 2) * (b - nrpyAbs(b))
if b == sp.sympify(0):
return sp.Rational(1, 2) * (a - nrpyAbs(a))
return sp.Rational(1, 2) * (a + b - nrpyAbs(a - b))
def Ar_SM(r, theta, phi, **params):
M = params["M"]
a = params["a"]
# A_r = -aC/8 * cos \theta ( 1 + 4M/r ) \sqrt{1 + 2M/r}
return -a * C_SM * sp.Rational(1, 8) * nrpyAbs(
sp.cos(theta)) * (sp.sympify(1) + sp.sympify(4) * M /
r) * sp.sqrt(sp.sympify(1) + sp.sympify(2) * M / r)
def u4U_in_terms_of_ValenciavU__rescale_ValenciavU_by_applying_speed_limit(
alpha, betaU, gammaDD, ValenciavU):
# Inputs: Metric lapse alpha, shift betaU, 3-metric gammaDD, Valencia 3-velocity ValenciavU
# Outputs (as globals): u4U_ito_ValenciavU, rescaledValenciavU
# R = gamma_{ij} v^i v^j
R = sp.sympify(0)
for i in range(3):
for j in range(3):
R += gammaDD[i][j] * ValenciavU[i] * ValenciavU[j]
thismodule = __name__
# The default value isn't terribly important here, since we can overwrite in the main C code
GAMMA_SPEED_LIMIT = par.Cparameters("REAL", thismodule,
"GAMMA_SPEED_LIMIT",
10.0) # Default value based on
# IllinoisGRMHD.
# GiRaFFE default = 2000.0
Rmax = 1 - 1 / (GAMMA_SPEED_LIMIT * GAMMA_SPEED_LIMIT)
# Now, we set Rstar = min(Rmax,R):
# If R < Rmax, then Rstar = 0.5*(Rmax+R-Rmax+R) = R
# If R >= Rmax, then Rstar = 0.5*(Rmax+R+Rmax-R) = Rmax
Rstar = sp.Rational(1, 2) * (Rmax + R - nrpyAbs(Rmax - R))
# We add TINYDOUBLE to R below to avoid a 0/0, which occurs when
# ValenciavU == 0 for all Valencia 3-velocity components.
# "Those tiny *doubles* make me warm all over
# with a feeling that I'm gonna love you till the end of time."
# - Adapted from Connie Francis' "Tiny Bubbles"
TINYDOUBLE = par.Cparameters("#define", thismodule, "TINYDOUBLE", 1e-100)
# The rescaled (speed-limited) Valencia 3-velocity
# is given by, v_{(n)}^i = sqrt{Rstar/R} v^i
global rescaledValenciavU
rescaledValenciavU = ixp.zerorank1()
for i in range(3):
# If R == 0, then Rstar == 0, so sqrt( Rstar/(R+TINYDOUBLE) )=sqrt(0/1e-100) = 0
# If your velocities are of order 1e-100 and this is physically
# meaningful, there must be something wrong with your unit conversion.
rescaledValenciavU[i] = ValenciavU[i] * sp.sqrt(Rstar /
(R + TINYDOUBLE))
# Finally compute u^mu in terms of Valenciav^i
# u^0 = 1/(alpha-sqrt(1-R^*))
global u4U_ito_ValenciavU
u4U_ito_ValenciavU = ixp.zerorank1(DIM=4)
u4U_ito_ValenciavU[0] = 1 / (alpha * sp.sqrt(1 - Rstar))
# u^i = u^0 ( alpha v^i_{(n)} - beta^i ), where v^i_{(n)} is the Valencia 3-velocity
for i in range(3):
u4U_ito_ValenciavU[i + 1] = u4U_ito_ValenciavU[0] * (
alpha * rescaledValenciavU[i] - betaU[i])
def Enforce_Detgammabar_Constraint_symb_expressions():
# Set spatial dimension (must be 3 for BSSN)
DIM = 3
# Then we set the coordinate system for the numerical grid
rfm.reference_metric(
) # Create ReU, ReDD needed for rescaling B-L initial data, generating BSSN RHSs, etc.
# We will need the h_{ij} quantities defined within BSSN_RHSs
# below when we enforce the gammahat=gammabar constraint
# Step 1: All barred quantities are defined in terms of BSSN rescaled gridfunctions,
# which we declare here in case they haven't yet been declared elsewhere.
Bq.declare_BSSN_gridfunctions_if_not_declared_already()
hDD = Bq.hDD
Bq.BSSN_basic_tensors()
gammabarDD = Bq.gammabarDD
# First define the Kronecker delta:
KroneckerDeltaDD = ixp.zerorank2()
for i in range(DIM):
KroneckerDeltaDD[i][i] = sp.sympify(1)
# The detgammabar in BSSN_RHSs is set to detgammahat when BSSN_RHSs::detgbarOverdetghat_equals_one=True (default),
# so we manually compute it here:
dummygammabarUU, detgammabar = ixp.symm_matrix_inverter3x3(gammabarDD)
# Next apply the constraint enforcement equation above.
hprimeDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
hprimeDD[i][j] = \
(nrpyAbs(rfm.detgammahat) / detgammabar) ** (sp.Rational(1, 3)) * (KroneckerDeltaDD[i][j] + hDD[i][j]) \
- KroneckerDeltaDD[i][j]
enforce_detg_constraint_symb_expressions = [
lhrh(lhs=gri.gfaccess("in_gfs", "hDD00"), rhs=hprimeDD[0][0]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD01"), rhs=hprimeDD[0][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD02"), rhs=hprimeDD[0][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD11"), rhs=hprimeDD[1][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD12"), rhs=hprimeDD[1][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD22"), rhs=hprimeDD[2][2])
]
return enforce_detg_constraint_symb_expressions
def Aph_SM(r, theta, phi, **params):
M = params["M"]
a = params["a"]
# A_\phi = M^2 C [1-\cos \theta + a^2 f(r) cos \theta sin^2 \theta]
return M * M * C_SM * (sp.sympify(1) - nrpyAbs(sp.cos(theta)) + a * a *
f_of_r(r, M) * sp.cos(theta) * sp.sin(theta)**2)
def GiRaFFE_NRPy_C2P(StildeD,BU,gammaDD,betaU,alpha):
GRHD.compute_sqrtgammaDET(gammaDD)
gammaUU,unusedgammadet = ixp.symm_matrix_inverter3x3(gammaDD)
BtildeU = ixp.zerorank1()
for i in range(3):
# \tilde{B}^i = B^i \sqrt{\gamma}
BtildeU[i] = GRHD.sqrtgammaDET*BU[i]
BtildeD = ixp.zerorank1()
for i in range(3):
for j in range(3):
BtildeD[j] += gammaDD[i][j]*BtildeU[i]
Btilde2 = sp.sympify(0)
for i in range(3):
Btilde2 += BtildeU[i]*BtildeD[i]
global outStildeD
outStildeD = StildeD
# Then, enforce the orthogonality:
if par.parval_from_str("enforce_orthogonality_StildeD_BtildeU"):
StimesB = sp.sympify(0)
for i in range(3):
StimesB += StildeD[i]*BtildeU[i]
for i in range(3):
# {\tilde S}_i = {\tilde S}_i - ({\tilde S}_j {\tilde B}^j) {\tilde B}_i/{\tilde B}^2
outStildeD[i] -= StimesB*BtildeD[i]/Btilde2
# Calculate \tilde{S}^2:
Stilde2 = sp.sympify(0)
for i in range(3):
for j in range(3):
Stilde2 += gammaUU[i][j]*outStildeD[i]*outStildeD[j]
# First we need to compute the factor f:
# f = \sqrt{(1-\Gamma_{\max}^{-2}){\tilde B}^4/(16 \pi^2 \gamma {\tilde S}^2)}
speed_limit_factor = sp.sqrt((sp.sympify(1)-GAMMA_SPEED_LIMIT**(-2.0))*Btilde2*Btilde2*sp.Rational(1,16)/\
(M_PI*M_PI*GRHD.sqrtgammaDET*GRHD.sqrtgammaDET*Stilde2))
import Min_Max_and_Piecewise_Expressions as noif
# Calculate B^2
B2 = sp.sympify(0)
for i in range(3):
for j in range(3):
B2 += gammaDD[i][j]*BU[i]*BU[j]
# Enforce the speed limit on StildeD:
if par.parval_from_str("enforce_speed_limit_StildeD"):
for i in range(3):
outStildeD[i] *= noif.min_noif(sp.sympify(1),speed_limit_factor)
global ValenciavU
ValenciavU = ixp.zerorank1()
# Recompute 3-velocity:
for i in range(3):
for j in range(3):
# \bar{v}^i = 4 \pi \gamma^{ij} {\tilde S}_j / (\sqrt{\gamma} B^2)
ValenciavU[i] += sp.sympify(4)*M_PI*gammaUU[i][j]*outStildeD[j]/(GRHD.sqrtgammaDET*B2)
# This number determines how far away (in grid points) we will apply the fix.
grid_points_from_z_plane = par.Cparameters("REAL",thismodule,"grid_points_from_z_plane",4.0)
if par.parval_from_str("enforce_current_sheet_prescription"):
# Calculate the drift velocity
driftvU = ixp.zerorank1()
for i in range(3):
driftvU[i] = alpha*ValenciavU[i] - betaU[i]
# The direct approach, used by the original GiRaFFE:
# v^z = -(\gamma_{xz} v^x + \gamma_{yz} v^y) / \gamma_{zz}
newdriftvU2 = -(gammaDD[0][2]*driftvU[0] + gammaDD[1][2]*driftvU[1])/gammaDD[2][2]
# Now that we have the z component, it's time to substitute its Valencia form in.
# Remember, we only do this if abs(z) < (k+0.01)*dz. Note that we add 0.01; this helps
# avoid floating point errors and division by zero. This is the same as abs(z) - (k+0.01)*dz<0
coord = nrpyAbs(rfm.xx[2])
bound =(grid_points_from_z_plane+sp.Rational(1,100))*gri.dxx[2]
ValenciavU[2] = noif.coord_leq_bound(coord,bound)*(newdriftvU2+betaU[2])/alpha \
+ noif.coord_greater_bound(coord,bound)*ValenciavU[2]