import numpy as np
from numba import njit
[docs]
@njit(cache=True)
def is_symmetric_pair(ng, nodal, sl, sr):
r"""
Are sl and sr symmetric about an axis parallel with the y-axis in the center of domain the x-direction?
Parameters
----------
ng : int
The number of ghost cells
nodal: bool
Is the data nodal?
sl, sr : ndarray
The two arrays to be compared
Returns
-------
out : int
Are they symmetric? (1 = yes, 0 = no)
"""
qx, qy = sl.shape
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
sym = 1
if not nodal:
done = False
for i in range(nx / 2):
il = ilo + i
ir = ihi - i
for j in range(jlo, jhi):
if (sl[il, j] != sr[ir, j]):
sym = 0
done = True
break
if done:
break
else:
done = False
for i in range(nx / 2):
il = ilo + i
ir = ihi - i + 1
for j in range(jlo, jhi):
if (sl[il, j] != sr[ir, j]):
sym = 0
done = True
break
if done:
break
return sym
[docs]
@njit(cache=True)
def is_symmetric(ng, nodal, s):
r"""
Is the left half of s the mirror image of the right half?
Parameters
----------
ng : int
The number of ghost cells
nodal: bool
Is the data nodal?
s : ndarray
The array to be compared
Returns
-------
out : int
Is it symmetric? (1 = yes, 0 = no)
"""
return is_symmetric_pair(ng, nodal, s, s)
[docs]
@njit(cache=True)
def is_asymmetric_pair(ng, nodal, sl, sr):
r"""
Are sl and sr asymmetric about an axis parallel with the y-axis in the center of domain the x-direction?
Parameters
----------
ng : int
The number of ghost cells
nodal: bool
Is the data nodal?
sl, sr : ndarray
The two arrays to be compared
Returns
-------
out : int
Are they asymmetric? (1 = yes, 0 = no)
"""
qx, qy = sl.shape
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
asym = 1
if not nodal:
done = False
for i in range(nx / 2):
for j in range(jlo, jhi):
il = ilo + i
ir = ihi - i
if (not sl[il, j] == -sr[ir, j]):
asym = 0
done = True
break
if done:
break
else:
done = False
for i in range(nx / 2):
for j in range(jlo, jhi):
il = ilo + i
ir = ihi - i + 1
# print *, il, ir, sl(il,j), -sr(ir,j)
if (not sl[il, j] == -sr[ir, j]):
asym = 0
done = True
break
if done:
break
return asym
[docs]
@njit(cache=True)
def is_asymmetric(ng, nodal, s):
"""
Is the left half of s asymmetric to the right half?
Parameters
----------
ng : int
The number of ghost cells
nodal: bool
Is the data nodal?
s : ndarray
The array to be compared
Returns
-------
out : int
Is it asymmetric? (1 = yes, 0 = no)
"""
return is_asymmetric_pair(ng, nodal, s, s)
[docs]
@njit(cache=True)
def mac_vels(ng, dx, dy, dt,
u, v,
ldelta_ux, ldelta_vx,
ldelta_uy, ldelta_vy,
gradp_x, gradp_y,
source):
r"""
Calculate the MAC velocities in the x and y directions.
Parameters
----------
ng : int
The number of ghost cells
dx, dy : float
The cell spacings
dt : float
The timestep
u, v : ndarray
x-velocity and y-velocity
ldelta_ux, ldelta_uy: ndarray
Limited slopes of the x-velocity in the x and y directions
ldelta_vx, ldelta_vy: ndarray
Limited slopes of the y-velocity in the x and y directions
gradp_x, gradp_y : ndarray
Pressure gradients in the x and y directions
source : ndarray
Source terms
Returns
-------
out : ndarray, ndarray
MAC velocities in the x and y directions
"""
# assertions
# print *, "checking ldelta_ux"
# if (not is_asymmetric(qx, qy, ng, .false., ldelta_ux) == 1):
# stop 'ldelta_ux not asymmetric'
#
# print *, "checking ldelta_uy"
# if (not is_symmetric(qx, qy, ng, .false., ldelta_uy) == 1):
# stop 'ldelta_uy not symmetric'
#
# print *, "checking ldelta_vx"
# if (not is_symmetric(qx, qy, ng, .false., ldelta_vx) == 1):
# stop 'ldelta_vx not symmetric'
#
# print *, "checking ldelta_vy"
# if (not is_symmetric(qx, qy, ng, .false., ldelta_vy) == 1):
# stop 'ldelta_vy not symmetric'
#
# print *, "checking gradp_x"
# if (not is_asymmetric(qx, qy, ng, .false., gradp_x) == 1):
# stop 'gradp_x not asymmetric'
#
# get the full u and v left and right states (including transverse
# terms) on both the x- and y-interfaces
# pylint: disable-next=unused-variable
u_xl, u_xr, u_yl, u_yr, v_xl, v_xr, v_yl, v_yr = get_interface_states(ng, dx, dy, dt,
u, v,
ldelta_ux, ldelta_vx,
ldelta_uy, ldelta_vy,
gradp_x, gradp_y,
source)
# print *, 'checking u_xl'
# if (not is_asymmetric_pair(qx, qy, ng, .true., u_xl, u_xr) == 1):
# stop 'u_xl/r not asymmetric'
#
# Riemann problem -- this follows Burger's equation. We don't use
# any input velocity for the upwinding. Also, we only care about
# the normal states here (u on x and v on y)
u_MAC = riemann_and_upwind(ng, u_xl, u_xr)
v_MAC = riemann_and_upwind(ng, v_yl, v_yr)
# print *, 'checking U_MAC'
# if (not is_asymmetric(qx, qy, ng, .true., u_MAC) == 1):
# stop 'u_MAC not asymmetric'
#
return u_MAC, v_MAC
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
[docs]
@njit(cache=True)
def states(ng, dx, dy, dt,
u, v,
ldelta_ux, ldelta_vx,
ldelta_uy, ldelta_vy,
gradp_x, gradp_y,
source,
u_MAC, v_MAC):
r"""
This is similar to ``mac_vels``, but it predicts the interface states
of both u and v on both interfaces, using the MAC velocities to
do the upwinding.
Parameters
----------
ng : int
The number of ghost cells
dx, dy : float
The cell spacings
dt : float
The timestep
u, v : ndarray
x-velocity and y-velocity
ldelta_ux, ldelta_uy: ndarray
Limited slopes of the x-velocity in the x and y directions
ldelta_vx, ldelta_vy: ndarray
Limited slopes of the y-velocity in the x and y directions
source : ndarray
Source terms
gradp_x, gradp_y : ndarray
Pressure gradients in the x and y directions
u_MAC, v_MAC : ndarray
MAC velocities in the x and y directions
Returns
-------
out : ndarray, ndarray, ndarray, ndarray
x and y velocities predicted to the interfaces
"""
# get the full u and v left and right states (including transverse
# terms) on both the x- and y-interfaces
u_xl, u_xr, u_yl, u_yr, v_xl, v_xr, v_yl, v_yr = get_interface_states(ng, dx, dy, dt,
u, v,
ldelta_ux, ldelta_vx,
ldelta_uy, ldelta_vy,
gradp_x, gradp_y,
source)
# upwind using the MAC velocity to determine which state exists on
# the interface
u_xint = upwind(ng, u_xl, u_xr, u_MAC)
v_xint = upwind(ng, v_xl, v_xr, u_MAC)
u_yint = upwind(ng, u_yl, u_yr, v_MAC)
v_yint = upwind(ng, v_yl, v_yr, v_MAC)
return u_xint, v_xint, u_yint, v_yint
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
[docs]
@njit(cache=True)
def rho_states(ng, dx, dy, dt,
rho, u_MAC, v_MAC,
ldelta_rx, ldelta_ry):
r"""
This predicts rho to the interfaces. We use the MAC velocities to do
the upwinding
Parameters
----------
ng : int
The number of ghost cells
dx, dy : float
The cell spacings
rho : ndarray
density
u_MAC, v_MAC : ndarray
MAC velocities in the x and y directions
ldelta_rx, ldelta_ry: ndarray
Limited slopes of the density in the x and y directions
Returns
-------
out : ndarray, ndarray
rho predicted to the interfaces
"""
qx, qy = rho.shape
rho_xl = np.zeros((qx, qy))
rho_xr = np.zeros((qx, qy))
rho_yl = np.zeros((qx, qy))
rho_yr = np.zeros((qx, qy))
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
dtdx = dt / dx
dtdy = dt / dy
for i in range(ilo - 2, ihi + 2):
for j in range(jlo - 2, jhi + 2):
# u on x-edges
rho_xl[i + 1, j] = rho[i, j] + 0.5 * \
(1.0 - dtdx * u_MAC[i + 1, j]) * ldelta_rx[i, j]
rho_xr[i, j] = rho[i, j] - 0.5 * \
(1.0 + dtdx * u_MAC[i, j]) * ldelta_rx[i, j]
# u on y-edges
rho_yl[i, j + 1] = rho[i, j] + 0.5 * \
(1.0 - dtdy * v_MAC[i, j + 1]) * ldelta_ry[i, j]
rho_yr[i, j] = rho[i, j] - 0.5 * \
(1.0 + dtdy * v_MAC[i, j]) * ldelta_ry[i, j]
# we upwind based on the MAC velocities
rho_xint = upwind(ng, rho_xl, rho_xr, u_MAC)
rho_yint = upwind(ng, rho_yl, rho_yr, v_MAC)
# now add the transverse term and the non-advective part of the normal
# divergence
for i in range(ilo - 2, ihi + 2):
for j in range(jlo - 2, jhi + 2):
u_x = (u_MAC[i + 1, j] - u_MAC[i, j]) / dx
v_y = (v_MAC[i, j + 1] - v_MAC[i, j]) / dy
# (rho v)_y is the transverse term for the x-interfaces
# rho u_x is the non-advective piece for the x-interfaces
rhov_y = (rho_yint[i, j + 1] * v_MAC[i, j + 1] -
rho_yint[i, j] * v_MAC[i, j]) / dy
rho_xl[i + 1, j] = rho_xl[i + 1, j] - \
0.5 * dt * (rhov_y + rho[i, j] * u_x)
rho_xr[i, j] = rho_xr[i, j] - 0.5 * dt * (rhov_y + rho[i, j] * u_x)
# (rho u)_x is the transverse term for the y-interfaces
# rho v_y is the non-advective piece for the y-interfaces
rhou_x = (rho_xint[i + 1, j] * u_MAC[i + 1, j] -
rho_xint[i, j] * u_MAC[i, j]) / dx
rho_yl[i, j + 1] = rho_yl[i, j + 1] - \
0.5 * dt * (rhou_x + rho[i, j] * v_y)
rho_yr[i, j] = rho_yr[i, j] - 0.5 * dt * (rhou_x + rho[i, j] * v_y)
# finally upwind the full states
rho_xint = upwind(ng, rho_xl, rho_xr, u_MAC)
rho_yint = upwind(ng, rho_yl, rho_yr, v_MAC)
return rho_xint, rho_yint
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
[docs]
@njit(cache=True)
def get_interface_states(ng, dx, dy, dt,
u, v,
ldelta_ux, ldelta_vx,
ldelta_uy, ldelta_vy,
gradp_x, gradp_y,
source):
r"""
Compute the unsplit predictions of u and v on both the x- and
y-interfaces. This includes the transverse terms.
Note that the ``gradp_x``, ``gradp_y`` should have any coefficients
already included (e.g. :math:`\beta_0/\rho`)
Parameters
----------
ng : int
The number of ghost cells
dx, dy : float
The cell spacings
dt : float
The timestep
u, v : ndarray
x-velocity and y-velocity
ldelta_ux, ldelta_uy: ndarray
Limited slopes of the x-velocity in the x and y directions
ldelta_vx, ldelta_vy: ndarray
Limited slopes of the y-velocity in the x and y directions
gradp_x, gradp_y : ndarray
Pressure gradients in the x and y directions
source : ndarray
Source terms
Returns
-------
out : ndarray, ndarray, ndarray, ndarray, ndarray, ndarray, ndarray, ndarray
unsplit predictions of u and v on both the x- and
y-interfaces
"""
qx, qy = u.shape
u_xl = np.zeros((qx, qy))
u_xr = np.zeros((qx, qy))
u_yl = np.zeros((qx, qy))
u_yr = np.zeros((qx, qy))
v_xl = np.zeros((qx, qy))
v_xr = np.zeros((qx, qy))
v_yl = np.zeros((qx, qy))
v_yr = np.zeros((qx, qy))
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
# first predict u and v to both interfaces, considering only the normal
# part of the predictor. These are the 'hat' states.
dtdx = dt / dx
dtdy = dt / dy
for i in range(ilo - 2, ihi + 2):
for j in range(jlo - 2, jhi + 2):
# u on x-edges
u_xl[i + 1, j] = u[i, j] + 0.5 * \
(1.0 - dtdx * u[i, j]) * ldelta_ux[i, j]
u_xr[i, j] = u[i, j] - 0.5 * \
(1.0 + dtdx * u[i, j]) * ldelta_ux[i, j]
# v on x-edges
v_xl[i + 1, j] = v[i, j] + 0.5 * \
(1.0 - dtdx * u[i, j]) * ldelta_vx[i, j]
v_xr[i, j] = v[i, j] - 0.5 * \
(1.0 + dtdx * u[i, j]) * ldelta_vx[i, j]
# u on y-edges
u_yl[i, j + 1] = u[i, j] + 0.5 * \
(1.0 - dtdy * v[i, j]) * ldelta_uy[i, j]
u_yr[i, j] = u[i, j] - 0.5 * \
(1.0 + dtdy * v[i, j]) * ldelta_uy[i, j]
# v on y-edges
v_yl[i, j + 1] = v[i, j] + 0.5 * \
(1.0 - dtdy * v[i, j]) * ldelta_vy[i, j]
v_yr[i, j] = v[i, j] - 0.5 * \
(1.0 + dtdy * v[i, j]) * ldelta_vy[i, j]
# print *, 'checking u_xl in states'
# if (not is_asymmetric_pair(qx, qy, ng, .true., u_xl, u_xr) == 1):
# stop 'u_xl/r not asymmetric'
#
# now get the normal advective velocities on the interfaces by solving
# the Riemann problem.
uhat_adv = riemann(ng, u_xl, u_xr)
vhat_adv = riemann(ng, v_yl, v_yr)
# now that we have the advective velocities, upwind the left and right
# states using the appropriate advective velocity.
# on the x-interfaces, we upwind based on uhat_adv
u_xint = upwind(ng, u_xl, u_xr, uhat_adv)
v_xint = upwind(ng, v_xl, v_xr, uhat_adv)
# on the y-interfaces, we upwind based on vhat_adv
u_yint = upwind(ng, u_yl, u_yr, vhat_adv)
v_yint = upwind(ng, v_yl, v_yr, vhat_adv)
# at this point, these states are the `hat' states -- they only
# considered the normal to the interface portion of the predictor.
# add the transverse flux differences to the preliminary interface states
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 1, jhi + 1):
ubar = 0.5 * (uhat_adv[i, j] + uhat_adv[i + 1, j])
vbar = 0.5 * (vhat_adv[i, j] + vhat_adv[i, j + 1])
# v du/dy is the transerse term for the u states on x-interfaces
vu_y = vbar * (u_yint[i, j + 1] - u_yint[i, j])
u_xl[i + 1, j] = u_xl[i + 1, j] - 0.5 * \
dtdy * vu_y - 0.5 * dt * gradp_x[i, j]
u_xr[i, j] = u_xr[i, j] - 0.5 * dtdy * \
vu_y - 0.5 * dt * gradp_x[i, j]
# v dv/dy is the transverse term for the v states on x-interfaces
vv_y = vbar * (v_yint[i, j + 1] - v_yint[i, j])
v_xl[i + 1, j] = v_xl[i + 1, j] - 0.5 * dtdy * vv_y - \
0.5 * dt * gradp_y[i, j] + 0.5 * dt * source[i, j]
v_xr[i, j] = v_xr[i, j] - 0.5 * dtdy * vv_y - 0.5 * \
dt * gradp_y[i, j] + 0.5 * dt * source[i, j]
# u dv/dx is the transverse term for the v states on y-interfaces
uv_x = ubar * (v_xint[i + 1, j] - v_xint[i, j])
v_yl[i, j + 1] = v_yl[i, j + 1] - 0.5 * dtdx * uv_x - \
0.5 * dt * gradp_y[i, j] + 0.5 * dt * source[i, j]
v_yr[i, j] = v_yr[i, j] - 0.5 * dtdx * uv_x - 0.5 * \
dt * gradp_y[i, j] + 0.5 * dt * source[i, j]
# u du/dx is the transverse term for the u states on y-interfaces
uu_x = ubar * (u_xint[i + 1, j] - u_xint[i, j])
u_yl[i, j + 1] = u_yl[i, j + 1] - 0.5 * \
dtdx * uu_x - 0.5 * dt * gradp_x[i, j]
u_yr[i, j] = u_yr[i, j] - 0.5 * dtdx * \
uu_x - 0.5 * dt * gradp_x[i, j]
return u_xl, u_xr, u_yl, u_yr, v_xl, v_xr, v_yl, v_yr
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
[docs]
@njit(cache=True)
def upwind(ng, q_l, q_r, s):
r"""
upwind the left and right states based on the specified input
velocity, s. The resulting interface state is q_int
Parameters
----------
ng : int
The number of ghost cells
q_l, q_r : ndarray
left and right states
s : ndarray
velocity
Returns
-------
q_int : ndarray
Upwinded state
"""
qx, qy = s.shape
q_int = np.zeros_like(s)
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
for i in range(ilo - 1, ihi + 2):
for j in range(jlo - 1, jhi + 2):
if (s[i, j] > 0.0):
q_int[i, j] = q_l[i, j]
elif (s[i, j] == 0.0):
q_int[i, j] = 0.5 * (q_l[i, j] + q_r[i, j])
else:
q_int[i, j] = q_r[i, j]
return q_int
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
[docs]
@njit(cache=True)
def riemann(ng, q_l, q_r):
"""
Solve the Burger's Riemann problem given the input left and right
states and return the state on the interface.
This uses the expressions from Almgren, Bell, and Szymczak 1996.
Parameters
----------
ng : int
The number of ghost cells
q_l, q_r : ndarray
left and right states
Returns
-------
out : ndarray
Interface state
"""
qx, qy = q_l.shape
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
s = np.zeros((qx, qy))
for i in range(ilo - 1, ihi + 2):
for j in range(jlo - 1, jhi + 2):
if q_l[i, j] > 0.0 and q_l[i, j] + q_r[i, j] > 0.0:
s[i, j] = q_l[i, j]
elif q_l[i, j] <= 0.0 and q_r[i, j] >= 0.0: # pylint: disable=chained-comparison
s[i, j] = 0.0
else:
s[i, j] = q_r[i, j]
return s
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
[docs]
@njit(cache=True)
def riemann_and_upwind(ng, q_l, q_r):
r"""
First solve the Riemann problem given q_l and q_r to give the
velocity on the interface and: use this velocity to upwind to
determine the state (q_l, q_r, or a mix) on the interface).
This differs from upwind, above, in that we don't take in a
velocity to upwind with).
Parameters
----------
ng : int
The number of ghost cells
q_l, q_r : ndarray
left and right states
Returns
-------
out : ndarray
Upwinded state
"""
s = riemann(ng, q_l, q_r)
return upwind(ng, q_l, q_r, s)
