import numpy as np
from numba import njit
[docs]
@njit(cache=True)
def states(a, ng, idir):
r"""
Predict the cell-centered state to the edges in one-dimension using the
reconstructed, limited slopes. We use a fourth-order Godunov method.
Our convention here is that:
``al[i,j]`` will be :math:`al_{i-1/2,j}`,
``al[i+1,j]`` will be :math:`al_{i+1/2,j}`.
Parameters
----------
a : ndarray
The cell-centered state.
ng : int
The number of ghost cells
idir : int
Are we predicting to the edges in the x-direction (1) or y-direction (2)?
Returns
-------
out : ndarray, ndarray
The state predicted to the left and right edges.
"""
# pylint: disable=too-many-nested-blocks
qx, qy = a.shape
al = np.zeros((qx, qy))
ar = np.zeros((qx, qy))
a_int = np.zeros((qx, qy))
dafm = np.zeros((qx, qy))
dafp = np.zeros((qx, qy))
d2af = np.zeros((qx, qy))
d2ac = np.zeros((qx, qy))
d3a = np.zeros((qx, qy))
C2 = 1.25
C3 = 0.1
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
# we need interface values on all faces of the domain
if idir == 1:
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# interpolate to the edges
a_int[i, j] = (7.0 / 12.0) * (a[i - 1, j] + a[i, j]) - \
(1.0 / 12.0) * (a[i - 2, j] + a[i + 1, j])
al[i, j] = a_int[i, j]
ar[i, j] = a_int[i, j]
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# these live on cell-centers
dafm[i, j] = a[i, j] - a_int[i, j]
dafp[i, j] = a_int[i + 1, j] - a[i, j]
# these live on cell-centers
d2af[i, j] = 6.0 * (a_int[i, j] - 2.0 *
a[i, j] + a_int[i + 1, j])
for i in range(ilo - 3, ihi + 3):
for j in range(jlo - 1, jhi + 1):
d2ac[i, j] = a[i - 1, j] - 2.0 * a[i, j] + a[i + 1, j]
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# this lives on the interface
d3a[i, j] = d2ac[i, j] - d2ac[i - 1, j]
# this is a look over cell centers, affecting
# i-1/2,R and i+1/2,L
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 1, jhi + 1):
# limit? MC Eq. 24 and 25
if (dafm[i, j] * dafp[i, j] <= 0.0 or
(a[i, j] - a[i - 2, j]) * (a[i + 2, j] - a[i, j]) <= 0.0):
# we are at an extrema
s = np.copysign(1.0, d2ac[i, j])
if (s == np.copysign(1.0, d2ac[i - 1, j]) and s == np.copysign(1.0, d2ac[i + 1, j]) and
s == np.copysign(1.0, d2af[i, j])):
# MC Eq. 26
d2a_lim = s * min(abs(d2af[i, j]), C2 * abs(d2ac[i - 1, j]),
C2 * abs(d2ac[i, j]), C2 * abs(d2ac[i + 1, j]))
else:
d2a_lim = 0.0
if (abs(d2af[i, j]) <= 1.e-12 * max(abs(a[i - 2, j]), abs(a[i - 1, j]),
abs(a[i, j]), abs(a[i + 1, j]), abs(a[i + 2, j]))):
rho = 0.0
else:
# MC Eq. 27
rho = d2a_lim / d2af[i, j]
if rho < 1.0 - 1.e-12:
# we may need to limit -- these quantities are at cell-centers
d3a_min = min(d3a[i - 1, j], d3a[i, j],
d3a[i + 1, j], d3a[i + 2, j])
d3a_max = max(d3a[i - 1, j], d3a[i, j],
d3a[i + 1, j], d3a[i + 2, j])
if C3 * max(abs(d3a_min), abs(d3a_max)) <= (d3a_max - d3a_min):
# limit
if (dafm[i, j] * dafp[i, j] < 0.0):
# Eqs. 29, 30
ar[i, j] = a[i, j] - rho * \
dafm[i, j] # note: typo in Eq 29
al[i + 1, j] = a[i, j] + rho * dafp[i, j]
elif (abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j])):
# Eq. 31
ar[i, j] = a[i, j] - 2.0 * \
(1.0 - rho) * dafp[i, j] - rho * dafm[i, j]
elif (abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j])):
# Eq. 32
al[i + 1, j] = a[i, j] + 2.0 * \
(1.0 - rho) * dafm[i, j] + rho * dafp[i, j]
else:
# if Eqs. 24 or 25 didn't hold we still may need to limit
if abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j]):
ar[i, j] = a[i, j] - 2.0 * dafp[i, j]
if abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j]):
al[i + 1, j] = a[i, j] + 2.0 * dafm[i, j]
elif idir == 2:
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 3):
# interpolate to the edges
a_int[i, j] = (7.0 / 12.0) * (a[i, j - 1] + a[i, j]) - \
(1.0 / 12.0) * (a[i, j - 2] + a[i, j + 1])
al[i, j] = a_int[i, j]
ar[i, j] = a_int[i, j]
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 3):
# these live on cell-centers
dafm[i, j] = a[i, j] - a_int[i, j]
dafp[i, j] = a_int[i, j + 1] - a[i, j]
# these live on cell-centers
d2af[i, j] = 6.0 * (a_int[i, j] - 2.0 *
a[i, j] + a_int[i, j + 1])
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 3, jhi + 3):
d2ac[i, j] = a[i, j - 1] - 2.0 * a[i, j] + a[i, j + 1]
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 2):
# this lives on the interface
d3a[i, j] = d2ac[i, j] - d2ac[i, j - 1]
# this is a look over cell centers, affecting
# j-1/2,R and j+1/2,L
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 1, jhi + 1):
# limit? MC Eq. 24 and 25
if (dafm[i, j] * dafp[i, j] <= 0.0 or
(a[i, j] - a[i, j - 2]) * (a[i, j + 2] - a[i, j]) <= 0.0):
# we are at an extrema
s = np.copysign(1.0, d2ac[i, j])
if (s == np.copysign(1.0, d2ac[i, j - 1]) and s == np.copysign(1.0, d2ac[i, j + 1]) and
s == np.copysign(1.0, d2af[i, j])):
# MC Eq. 26
d2a_lim = s * min(abs(d2af[i, j]), C2 * abs(d2ac[i, j - 1]),
C2 * abs(d2ac[i, j]), C2 * abs(d2ac[i, j + 1]))
else:
d2a_lim = 0.0
if (abs(d2af[i, j]) <= 1.e-12 * max(abs(a[i, j - 2]), abs(a[i, j - 1]),
abs(a[i, j]), abs(a[i, j + 1]), abs(a[i, j + 2]))):
rho = 0.0
else:
# MC Eq. 27
rho = d2a_lim / d2af[i, j]
if rho < 1.0 - 1.e-12:
# we may need to limit -- these quantities are at cell-centers
d3a_min = min(d3a[i, j - 1], d3a[i, j],
d3a[i, j + 1], d3a[i, j + 2])
d3a_max = max(d3a[i, j - 1], d3a[i, j],
d3a[i, j + 1], d3a[i, j + 2])
if C3 * max(abs(d3a_min), abs(d3a_max)) <= (d3a_max - d3a_min):
# limit
if (dafm[i, j] * dafp[i, j] < 0.0):
# Eqs. 29, 30
ar[i, j] = a[i, j] - rho * \
dafm[i, j] # note: typo in Eq 29
al[i, j + 1] = a[i, j] + rho * dafp[i, j]
elif (abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j])):
# Eq. 31
ar[i, j] = a[i, j] - 2.0 * \
(1.0 - rho) * dafp[i, j] - rho * dafm[i, j]
elif (abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j])):
# Eq. 32
al[i, j + 1] = a[i, j] + 2.0 * \
(1.0 - rho) * dafm[i, j] + rho * dafp[i, j]
else:
# if Eqs. 24 or 25 didn't hold we still may need to limit
if abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j]):
ar[i, j] = a[i, j] - 2.0 * dafp[i, j]
if abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j]):
al[i, j + 1] = a[i, j] + 2.0 * dafm[i, j]
return al, ar
[docs]
@njit(cache=True)
def states_nolimit(a, qx, qy, ng, idir):
r"""
Predict the cell-centered state to the edges in one-dimension using the
reconstructed slopes (and without slope limiting). We use a fourth-order
Godunov method.
Our convention here is that:
``al[i,j]`` will be :math:`al_{i-1/2,j}`,
``al[i+1,j]`` will be :math:`al_{i+1/2,j}`.
Parameters
----------
a : ndarray
The cell-centered state.
ng : int
The number of ghost cells
idir : int
Are we predicting to the edges in the x-direction (1) or y-direction (2)?
Returns
-------
out : ndarray, ndarray
The state predicted to the left and right edges.
"""
a_int = np.zeros((qx, qy))
al = np.zeros((qx, qy))
ar = np.zeros((qx, qy))
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
# we need interface values on all faces of the domain
if idir == 1:
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# interpolate to the edges
a_int[i, j] = (7.0 / 12.0) * (a[i - 1, j] + a[i, j]) - \
(1.0 / 12.0) * (a[i - 2, j] + a[i + 1, j])
al[i, j] = a_int[i, j]
ar[i, j] = a_int[i, j]
elif idir == 2:
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 3):
# interpolate to the edges
a_int[i, j] = (7.0 / 12.0) * (a[i, j - 1] + a[i, j]) - \
(1.0 / 12.0) * (a[i, j - 2] + a[i, j + 1])
al[i, j] = a_int[i, j]
ar[i, j] = a_int[i, j]
return al, ar
