def dovis(self):
"""
Do runtime visualization.
"""
plt.clf()
plt.rc("font", size=10)
# we do this even though ivars is in self, so this works when
# we are plotting from a file
ivars = compressible.Variables(self.cc_data)
# access gamma from the cc_data object so we can use dovis
# outside of a running simulation.
gamma = self.cc_data.get_aux("gamma")
q = compressible.cons_to_prim(self.cc_data.data, gamma, ivars, self.cc_data.grid)
rho = q[:,:,ivars.irho]
u = q[:,:,ivars.iu]
v = q[:,:,ivars.iv]
p = q[:,:,ivars.ip]
e = eos.rhoe(gamma, p)/rho
X = q[:,:,ivars.ix]
magvel = np.sqrt(u**2 + v**2)
myg = self.cc_data.grid
fields = [rho, magvel, p, e, X]
field_names = [r"$\rho$", r"U", "p", "e", r"$X_\mathrm{fuel}$"]
f, axes, cbar_title = plot_tools.setup_axes(myg, len(fields))
for n, ax in enumerate(axes):
v = fields[n]
img = ax.imshow(np.transpose(v.v()),
interpolation="nearest", origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax],
cmap=self.cm)
ax.set_xlabel("x")
ax.set_ylabel("y")
# needed for PDF rendering
cb = axes.cbar_axes[n].colorbar(img)
cb.solids.set_rasterized(True)
cb.solids.set_edgecolor("face")
if cbar_title:
cb.ax.set_title(field_names[n])
else:
ax.set_title(field_names[n])
plt.figtext(0.05, 0.0125, "t = {:10.5g}".format(self.cc_data.t))
plt.pause(0.001)
plt.draw()
def fluxes(my_data, rp, ivars, solid, tc):
"""
unsplitFluxes returns the fluxes through the x and y interfaces by
doing an unsplit reconstruction of the interface values and then
solving the Riemann problem through all the interfaces at once
currently we assume a gamma-law EOS
Parameters
----------
my_data : CellCenterData2d object
The data object containing the grid and advective scalar that
we are advecting.
rp : RuntimeParameters object
The runtime parameters for the simulation
vars : Variables object
The Variables object that tells us which indices refer to which
variables
tc : TimerCollection object
The timers we are using to profile
Returns
-------
out : ndarray, ndarray
The fluxes on the x- and y-interfaces
"""
tm_flux = tc.timer("unsplitFluxes")
tm_flux.begin()
myg = my_data.grid
gamma = rp.get_param("eos.gamma")
# =========================================================================
# compute the primitive variables
# =========================================================================
# Q = (rho, u, v, p)
q = comp.cons_to_prim(my_data.data, gamma, ivars, myg)
# =========================================================================
# compute the flattening coefficients
# =========================================================================
# there is a single flattening coefficient (xi) for all directions
use_flattening = rp.get_param("compressible.use_flattening")
if use_flattening:
xi_x = reconstruction.flatten(myg, q, 1, ivars, rp)
xi_y = reconstruction.flatten(myg, q, 2, ivars, rp)
xi = reconstruction.flatten_multid(myg, q, xi_x, xi_y, ivars)
else:
xi = 1.0
# monotonized central differences in x-direction
tm_limit = tc.timer("limiting")
tm_limit.begin()
limiter = rp.get_param("compressible.limiter")
ldx = myg.scratch_array(nvar=ivars.nvar)
ldy = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
ldx[:, :, n] = xi * reconstruction.limit(q[:, :, n], myg, 1, limiter)
ldy[:, :, n] = xi * reconstruction.limit(q[:, :, n], myg, 2, limiter)
tm_limit.end()
# if we are doing a well-balanced scheme, then redo the pressure
# note: we only have gravity in the y direction, so we will only
# modify the y pressure slope
well_balanced = rp.get_param("compressible.well_balanced")
grav = rp.get_param("compressible.grav")
if well_balanced:
ldy[:, :,
ivars.ip] = reconstruction.well_balance(q, myg, limiter, ivars,
grav)
# =========================================================================
# x-direction
# =========================================================================
# left and right primitive variable states
tm_states = tc.timer("interfaceStates")
tm_states.begin()
V_l = myg.scratch_array(ivars.nvar)
V_r = myg.scratch_array(ivars.nvar)
for n in range(ivars.nvar):
V_l.ip(1, n=n, buf=2)[:, :] = q.v(n=n, buf=2) + 0.5 * ldx.v(n=n, buf=2)
V_r.v(n=n, buf=2)[:, :] = q.v(n=n, buf=2) - 0.5 * ldx.v(n=n, buf=2)
tm_states.end()
# transform interface states back into conserved variables
U_xl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_xr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# y-direction
# =========================================================================
# left and right primitive variable states
tm_states.begin()
for n in range(ivars.nvar):
if well_balanced and n == ivars.ip:
# we want to do p0 + p1 on the interfaces. We found the
# limited slope for p1 (it's average is 0). So now we
# need p0 on the interface too
V_l.jp(1, n=n, buf=2)[:, :] = q.v(n=ivars.ip, buf=2) + \
0.5 * myg.dy * q.v(n=ivars.irho, buf=2) * \
grav + 0.5 * ldy.v(n=ivars.ip, buf=2)
V_r.v(n=n, buf=2)[:, :] = q.v(n=ivars.ip, buf=2) - \
0.5 * myg.dy * q.v(n=ivars.irho, buf=2) * \
grav - 0.5 * ldy.v(n=ivars.ip, buf=2)
else:
V_l.jp(1, n=n,
buf=2)[:, :] = q.v(n=n, buf=2) + 0.5 * ldy.v(n=n, buf=2)
V_r.v(n=n, buf=2)[:, :] = q.v(n=n, buf=2) - 0.5 * ldy.v(n=n, buf=2)
tm_states.end()
# transform interface states back into conserved variables
U_yl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_yr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# construct the fluxes normal to the interfaces
# =========================================================================
tm_riem = tc.timer("Riemann")
tm_riem.begin()
riemann = rp.get_param("compressible.riemann")
if riemann == "HLLC":
riemannFunc = interface.riemann_hllc
elif riemann == "CGF":
riemannFunc = interface.riemann_cgf
else:
msg.fail("ERROR: Riemann solver undefined")
_fx = riemannFunc(1, myg.ng, ivars.idens, ivars.ixmom, ivars.iymom,
ivars.iener, ivars.irhox, ivars.naux, solid.xl, solid.xr,
gamma, U_xl, U_xr)
_fy = riemannFunc(2, myg.ng, ivars.idens, ivars.ixmom, ivars.iymom,
ivars.iener, ivars.irhox, ivars.naux, solid.yl, solid.yr,
gamma, U_yl, U_yr)
F_x = ai.ArrayIndexer(d=_fx, grid=myg)
F_y = ai.ArrayIndexer(d=_fy, grid=myg)
tm_riem.end()
# =========================================================================
# apply artificial viscosity
# =========================================================================
cvisc = rp.get_param("compressible.cvisc")
_ax, _ay = interface.artificial_viscosity(myg.ng, myg.dx, myg.dy, cvisc,
q.v(n=ivars.iu, buf=myg.ng),
q.v(n=ivars.iv, buf=myg.ng))
avisco_x = ai.ArrayIndexer(d=_ax, grid=myg)
avisco_y = ai.ArrayIndexer(d=_ay, grid=myg)
b = (2, 1)
for n in range(ivars.nvar):
# F_x = F_x + avisco_x * (U(i-1,j) - U(i,j))
var = my_data.get_var_by_index(n)
F_x.v(buf=b, n=n)[:, :] += \
avisco_x.v(buf=b) * (var.ip(-1, buf=b) - var.v(buf=b))
# F_y = F_y + avisco_y * (U(i,j-1) - U(i,j))
F_y.v(buf=b, n=n)[:, :] += \
avisco_y.v(buf=b) * (var.jp(-1, buf=b) - var.v(buf=b))
tm_flux.end()
return F_x, F_y
def fluxes(my_data, rp, ivars, solid, tc):
"""
unsplitFluxes returns the fluxes through the x and y interfaces by
doing an unsplit reconstruction of the interface values and then
solving the Riemann problem through all the interfaces at once
currently we assume a gamma-law EOS
Parameters
----------
my_data : CellCenterData2d object
The data object containing the grid and advective scalar that
we are advecting.
rp : RuntimeParameters object
The runtime parameters for the simulation
vars : Variables object
The Variables object that tells us which indices refer to which
variables
tc : TimerCollection object
The timers we are using to profile
Returns
-------
out : ndarray, ndarray
The fluxes on the x- and y-interfaces
"""
tm_flux = tc.timer("unsplitFluxes")
tm_flux.begin()
myg = my_data.grid
gamma = rp.get_param("eos.gamma")
# =========================================================================
# compute the primitive variables
# =========================================================================
# Q = (rho, u, v, p)
q = comp.cons_to_prim(my_data.data, gamma, ivars, myg)
# =========================================================================
# compute the flattening coefficients
# =========================================================================
# there is a single flattening coefficient (xi) for all directions
use_flattening = rp.get_param("compressible.use_flattening")
if use_flattening:
xi_x = reconstruction.flatten(myg, q, 1, ivars, rp)
xi_y = reconstruction.flatten(myg, q, 2, ivars, rp)
xi = reconstruction.flatten_multid(myg, q, xi_x, xi_y, ivars)
else:
xi = 1.0
# monotonized central differences in x-direction
tm_limit = tc.timer("limiting")
tm_limit.begin()
limiter = rp.get_param("compressible.limiter")
ldx = myg.scratch_array(nvar=ivars.nvar)
ldy = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
ldx[:, :, n] = xi * reconstruction.limit(q[:, :, n], myg, 1, limiter)
ldy[:, :, n] = xi * reconstruction.limit(q[:, :, n], myg, 2, limiter)
tm_limit.end()
# if we are doing a well-balanced scheme, then redo the pressure
# note: we only have gravity in the y direction, so we will only
# modify the y pressure slope
well_balanced = rp.get_param("compressible.well_balanced")
grav = rp.get_param("compressible.grav")
if well_balanced:
ldy[:, :, ivars.ip] = reconstruction.well_balance(
q, myg, limiter, ivars, grav)
# =========================================================================
# x-direction
# =========================================================================
# left and right primitive variable states
tm_states = tc.timer("interfaceStates")
tm_states.begin()
V_l = myg.scratch_array(ivars.nvar)
V_r = myg.scratch_array(ivars.nvar)
for n in range(ivars.nvar):
V_l.ip(1, n=n, buf=2)[:, :] = q.v(n=n, buf=2) + 0.5 * ldx.v(n=n, buf=2)
V_r.v(n=n, buf=2)[:, :] = q.v(n=n, buf=2) - 0.5 * ldx.v(n=n, buf=2)
tm_states.end()
# transform interface states back into conserved variables
U_xl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_xr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# y-direction
# =========================================================================
# left and right primitive variable states
tm_states.begin()
for n in range(ivars.nvar):
if well_balanced and n == ivars.ip:
# we want to do p0 + p1 on the interfaces. We found the
# limited slope for p1 (it's average is 0). So now we
# need p0 on the interface too
V_l.jp(1, n=n, buf=2)[:, :] = q.v(n=ivars.ip, buf=2) + \
0.5 * myg.dy * q.v(n=ivars.irho, buf=2) * \
grav + 0.5 * ldy.v(n=ivars.ip, buf=2)
V_r.v(n=n, buf=2)[:, :] = q.v(n=ivars.ip, buf=2) - \
0.5 * myg.dy * q.v(n=ivars.irho, buf=2) * \
grav - 0.5 * ldy.v(n=ivars.ip, buf=2)
else:
V_l.jp(1, n=n, buf=2)[:, :] = q.v(
n=n, buf=2) + 0.5 * ldy.v(n=n, buf=2)
V_r.v(n=n, buf=2)[:, :] = q.v(n=n, buf=2) - 0.5 * ldy.v(n=n, buf=2)
tm_states.end()
# transform interface states back into conserved variables
U_yl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_yr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# construct the fluxes normal to the interfaces
# =========================================================================
tm_riem = tc.timer("Riemann")
tm_riem.begin()
riemann = rp.get_param("compressible.riemann")
if riemann == "HLLC":
riemannFunc = interface.riemann_hllc
elif riemann == "CGF":
riemannFunc = interface.riemann_cgf
else:
msg.fail("ERROR: Riemann solver undefined")
_fx = riemannFunc(1, myg.ng,
ivars.idens, ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox, ivars.naux,
solid.xl, solid.xr,
gamma, U_xl, U_xr)
_fy = riemannFunc(2, myg.ng,
ivars.idens, ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox, ivars.naux,
solid.yl, solid.yr,
gamma, U_yl, U_yr)
F_x = ai.ArrayIndexer(d=_fx, grid=myg)
F_y = ai.ArrayIndexer(d=_fy, grid=myg)
tm_riem.end()
# =========================================================================
# apply artificial viscosity
# =========================================================================
cvisc = rp.get_param("compressible.cvisc")
_ax, _ay = interface.artificial_viscosity(myg.ng, myg.dx, myg.dy,
cvisc, q.v(n=ivars.iu, buf=myg.ng), q.v(n=ivars.iv, buf=myg.ng))
avisco_x = ai.ArrayIndexer(d=_ax, grid=myg)
avisco_y = ai.ArrayIndexer(d=_ay, grid=myg)
b = (2, 1)
for n in range(ivars.nvar):
# F_x = F_x + avisco_x * (U(i-1,j) - U(i,j))
var = my_data.get_var_by_index(n)
F_x.v(buf=b, n=n)[:, :] += \
avisco_x.v(buf=b) * (var.ip(-1, buf=b) - var.v(buf=b))
# F_y = F_y + avisco_y * (U(i,j-1) - U(i,j))
F_y.v(buf=b, n=n)[:, :] += \
avisco_y.v(buf=b) * (var.jp(-1, buf=b) - var.v(buf=b))
tm_flux.end()
return F_x, F_y
def unsplit_fluxes(my_data, my_aux, rp, ivars, solid, tc, dt):
"""
unsplitFluxes returns the fluxes through the x and y interfaces by
doing an unsplit reconstruction of the interface values and then
solving the Riemann problem through all the interfaces at once
currently we assume a gamma-law EOS
The runtime parameter grav is assumed to be the gravitational
acceleration in the y-direction
Parameters
----------
my_data : CellCenterData2d object
The data object containing the grid and advective scalar that
we are advecting.
rp : RuntimeParameters object
The runtime parameters for the simulation
vars : Variables object
The Variables object that tells us which indices refer to which
variables
tc : TimerCollection object
The timers we are using to profile
dt : float
The timestep we are advancing through.
Returns
-------
out : ndarray, ndarray
The fluxes on the x- and y-interfaces
"""
tm_flux = tc.timer("unsplitFluxes")
tm_flux.begin()
myg = my_data.grid
gamma = rp.get_param("eos.gamma")
# =========================================================================
# compute the primitive variables
# =========================================================================
# Q = (rho, u, v, p, {X})
dens = my_data.get_var("density")
ymom = my_data.get_var("y-momentum")
q = comp.cons_to_prim(my_data.data, gamma, ivars, myg)
# =========================================================================
# compute the flattening coefficients
# =========================================================================
# there is a single flattening coefficient (xi) for all directions
use_flattening = rp.get_param("compressible.use_flattening")
if use_flattening:
xi_x = reconstruction.flatten(myg, q, 1, ivars, rp)
xi_y = reconstruction.flatten(myg, q, 2, ivars, rp)
xi = reconstruction.flatten_multid(myg, q, xi_x, xi_y, ivars)
else:
xi = 1.0
# monotonized central differences
tm_limit = tc.timer("limiting")
tm_limit.begin()
limiter = rp.get_param("compressible.limiter")
ldx = myg.scratch_array(nvar=ivars.nvar)
ldy = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
ldx[:, :, n] = xi*reconstruction.limit(q[:, :, n], myg, 1, limiter)
ldy[:, :, n] = xi*reconstruction.limit(q[:, :, n], myg, 2, limiter)
tm_limit.end()
# =========================================================================
# x-direction
# =========================================================================
# left and right primitive variable states
tm_states = tc.timer("interfaceStates")
tm_states.begin()
V_l, V_r = ifc.states(1, myg.ng, myg.dx, dt,
ivars.irho, ivars.iu, ivars.iv, ivars.ip, ivars.ix,
ivars.naux,
gamma,
q, ldx)
tm_states.end()
# transform interface states back into conserved variables
U_xl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_xr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# y-direction
# =========================================================================
# left and right primitive variable states
tm_states.begin()
_V_l, _V_r = ifc.states(2, myg.ng, myg.dy, dt,
ivars.irho, ivars.iu, ivars.iv, ivars.ip, ivars.ix,
ivars.naux,
gamma,
q, ldy)
V_l = ai.ArrayIndexer(d=_V_l, grid=myg)
V_r = ai.ArrayIndexer(d=_V_r, grid=myg)
tm_states.end()
# transform interface states back into conserved variables
U_yl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_yr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# apply source terms
# =========================================================================
grav = rp.get_param("compressible.grav")
ymom_src = my_aux.get_var("ymom_src")
ymom_src.v()[:, :] = dens.v()*grav
my_aux.fill_BC("ymom_src")
E_src = my_aux.get_var("E_src")
E_src.v()[:, :] = ymom.v()*grav
my_aux.fill_BC("E_src")
# ymom_xl[i,j] += 0.5*dt*dens[i-1,j]*grav
U_xl.v(buf=1, n=ivars.iymom)[:, :] += 0.5*dt*ymom_src.ip(-1, buf=1)
U_xl.v(buf=1, n=ivars.iener)[:, :] += 0.5*dt*E_src.ip(-1, buf=1)
# ymom_xr[i,j] += 0.5*dt*dens[i,j]*grav
U_xr.v(buf=1, n=ivars.iymom)[:, :] += 0.5*dt*ymom_src.v(buf=1)
U_xr.v(buf=1, n=ivars.iener)[:, :] += 0.5*dt*E_src.v(buf=1)
# ymom_yl[i,j] += 0.5*dt*dens[i,j-1]*grav
U_yl.v(buf=1, n=ivars.iymom)[:, :] += 0.5*dt*ymom_src.jp(-1, buf=1)
U_yl.v(buf=1, n=ivars.iener)[:, :] += 0.5*dt*E_src.jp(-1, buf=1)
# ymom_yr[i,j] += 0.5*dt*dens[i,j]*grav
U_yr.v(buf=1, n=ivars.iymom)[:, :] += 0.5*dt*ymom_src.v(buf=1)
U_yr.v(buf=1, n=ivars.iener)[:, :] += 0.5*dt*E_src.v(buf=1)
# =========================================================================
# compute transverse fluxes
# =========================================================================
tm_riem = tc.timer("riemann")
tm_riem.begin()
riemann = rp.get_param("compressible.riemann")
if riemann == "HLLC":
riemannFunc = ifc.riemann_hllc
elif riemann == "CGF":
riemannFunc = ifc.riemann_cgf
else:
msg.fail("ERROR: Riemann solver undefined")
_fx = riemannFunc(1, myg.ng,
ivars.idens, ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox, ivars.naux,
solid.xl, solid.xr,
gamma, U_xl, U_xr)
_fy = riemannFunc(2, myg.ng,
ivars.idens, ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox, ivars.naux,
solid.yl, solid.yr,
gamma, U_yl, U_yr)
F_x = ai.ArrayIndexer(d=_fx, grid=myg)
F_y = ai.ArrayIndexer(d=_fy, grid=myg)
tm_riem.end()
# =========================================================================
# construct the interface values of U now
# =========================================================================
"""
finally, we can construct the state perpendicular to the interface
by adding the central difference part to the trasverse flux
difference.
The states that we represent by indices i,j are shown below
(1,2,3,4):
j+3/2--+----------+----------+----------+
| | | |
| | | |
j+1 -+ | | |
| | | |
| | | | 1: U_xl[i,j,:] = U
j+1/2--+----------XXXXXXXXXXXX----------+ i-1/2,j,L
| X X |
| X X |
j -+ 1 X 2 X | 2: U_xr[i,j,:] = U
| X X | i-1/2,j,R
| X 4 X |
j-1/2--+----------XXXXXXXXXXXX----------+
| | 3 | | 3: U_yl[i,j,:] = U
| | | | i,j-1/2,L
j-1 -+ | | |
| | | |
| | | | 4: U_yr[i,j,:] = U
j-3/2--+----------+----------+----------+ i,j-1/2,R
| | | | | | |
i-1 i i+1
i-3/2 i-1/2 i+1/2 i+3/2
remember that the fluxes are stored on the left edge, so
F_x[i,j,:] = F_x
i-1/2, j
F_y[i,j,:] = F_y
i, j-1/2
"""
tm_transverse = tc.timer("transverse flux addition")
tm_transverse.begin()
dtdx = dt/myg.dx
dtdy = dt/myg.dy
b = (2, 1)
for n in range(ivars.nvar):
# U_xl[i,j,:] = U_xl[i,j,:] - 0.5*dt/dy * (F_y[i-1,j+1,:] - F_y[i-1,j,:])
U_xl.v(buf=b, n=n)[:, :] += \
- 0.5*dtdy*(F_y.ip_jp(-1, 1, buf=b, n=n) - F_y.ip(-1, buf=b, n=n))
# U_xr[i,j,:] = U_xr[i,j,:] - 0.5*dt/dy * (F_y[i,j+1,:] - F_y[i,j,:])
U_xr.v(buf=b, n=n)[:, :] += \
- 0.5*dtdy*(F_y.jp(1, buf=b, n=n) - F_y.v(buf=b, n=n))
# U_yl[i,j,:] = U_yl[i,j,:] - 0.5*dt/dx * (F_x[i+1,j-1,:] - F_x[i,j-1,:])
U_yl.v(buf=b, n=n)[:, :] += \
- 0.5*dtdx*(F_x.ip_jp(1, -1, buf=b, n=n) - F_x.jp(-1, buf=b, n=n))
# U_yr[i,j,:] = U_yr[i,j,:] - 0.5*dt/dx * (F_x[i+1,j,:] - F_x[i,j,:])
U_yr.v(buf=b, n=n)[:, :] += \
- 0.5*dtdx*(F_x.ip(1, buf=b, n=n) - F_x.v(buf=b, n=n))
tm_transverse.end()
# =========================================================================
# construct the fluxes normal to the interfaces
# =========================================================================
# up until now, F_x and F_y stored the transverse fluxes, now we
# overwrite with the fluxes normal to the interfaces
tm_riem.begin()
_fx = riemannFunc(1, myg.ng,
ivars.idens, ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox, ivars.naux,
solid.xl, solid.xr,
gamma, U_xl, U_xr)
_fy = riemannFunc(2, myg.ng,
ivars.idens, ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox, ivars.naux,
solid.yl, solid.yr,
gamma, U_yl, U_yr)
F_x = ai.ArrayIndexer(d=_fx, grid=myg)
F_y = ai.ArrayIndexer(d=_fy, grid=myg)
tm_riem.end()
# =========================================================================
# apply artificial viscosity
# =========================================================================
cvisc = rp.get_param("compressible.cvisc")
_ax, _ay = ifc.artificial_viscosity(myg.ng, myg.dx, myg.dy,
cvisc, q.v(n=ivars.iu, buf=myg.ng), q.v(n=ivars.iv, buf=myg.ng))
avisco_x = ai.ArrayIndexer(d=_ax, grid=myg)
avisco_y = ai.ArrayIndexer(d=_ay, grid=myg)
b = (2, 1)
for n in range(ivars.nvar):
# F_x = F_x + avisco_x * (U(i-1,j) - U(i,j))
var = my_data.get_var_by_index(n)
F_x.v(buf=b, n=n)[:, :] += \
avisco_x.v(buf=b)*(var.ip(-1, buf=b) - var.v(buf=b))
# F_y = F_y + avisco_y * (U(i,j-1) - U(i,j))
F_y.v(buf=b, n=n)[:, :] += \
avisco_y.v(buf=b)*(var.jp(-1, buf=b) - var.v(buf=b))
tm_flux.end()
return F_x, F_y
def unsplit_fluxes(my_data, my_aux, rp, ivars, solid, tc, dt):
"""
unsplitFluxes returns the fluxes through the x and y interfaces by
doing an unsplit reconstruction of the interface values and then
solving the Riemann problem through all the interfaces at once
currently we assume a gamma-law EOS
The runtime parameter grav is assumed to be the gravitational
acceleration in the y-direction
Parameters
----------
my_data : CellCenterData2d object
The data object containing the grid and advective scalar that
we are advecting.
rp : RuntimeParameters object
The runtime parameters for the simulation
vars : Variables object
The Variables object that tells us which indices refer to which
variables
tc : TimerCollection object
The timers we are using to profile
dt : float
The timestep we are advancing through.
Returns
-------
out : ndarray, ndarray
The fluxes on the x- and y-interfaces
"""
tm_flux = tc.timer("unsplitFluxes")
tm_flux.begin()
myg = my_data.grid
gamma = rp.get_param("eos.gamma")
# =========================================================================
# compute the primitive variables
# =========================================================================
# Q = (rho, u, v, p, {X})
dens = my_data.get_var("density")
ymom = my_data.get_var("y-momentum")
q = comp.cons_to_prim(my_data.data, gamma, ivars, myg)
# =========================================================================
# compute the flattening coefficients
# =========================================================================
# there is a single flattening coefficient (xi) for all directions
use_flattening = rp.get_param("compressible.use_flattening")
if use_flattening:
xi_x = reconstruction.flatten(myg, q, 1, ivars, rp)
xi_y = reconstruction.flatten(myg, q, 2, ivars, rp)
xi = reconstruction.flatten_multid(myg, q, xi_x, xi_y, ivars)
else:
xi = 1.0
# monotonized central differences
tm_limit = tc.timer("limiting")
tm_limit.begin()
limiter = rp.get_param("compressible.limiter")
ldx = myg.scratch_array(nvar=ivars.nvar)
ldy = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
ldx[:, :, n] = xi * reconstruction.limit(q[:, :, n], myg, 1, limiter)
ldy[:, :, n] = xi * reconstruction.limit(q[:, :, n], myg, 2, limiter)
tm_limit.end()
# =========================================================================
# x-direction
# =========================================================================
# left and right primitive variable states
tm_states = tc.timer("interfaceStates")
tm_states.begin()
V_l, V_r = ifc.states(1, myg.qx, myg.qy, myg.ng, myg.dx, dt, ivars.irho,
ivars.iu, ivars.iv, ivars.ip, ivars.ix, ivars.nvar,
ivars.naux, gamma, q, ldx)
tm_states.end()
# transform interface states back into conserved variables
U_xl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_xr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# y-direction
# =========================================================================
# left and right primitive variable states
tm_states.begin()
_V_l, _V_r = ifc.states(2, myg.qx, myg.qy, myg.ng, myg.dy, dt, ivars.irho,
ivars.iu, ivars.iv, ivars.ip, ivars.ix, ivars.nvar,
ivars.naux, gamma, q, ldy)
V_l = ai.ArrayIndexer(d=_V_l, grid=myg)
V_r = ai.ArrayIndexer(d=_V_r, grid=myg)
tm_states.end()
# transform interface states back into conserved variables
U_yl = comp.prim_to_cons(V_l, gamma, ivars, myg)
U_yr = comp.prim_to_cons(V_r, gamma, ivars, myg)
# =========================================================================
# apply source terms
# =========================================================================
grav = rp.get_param("compressible.grav")
ymom_src = my_aux.get_var("ymom_src")
ymom_src.v()[:, :] = dens.v() * grav
my_aux.fill_BC("ymom_src")
E_src = my_aux.get_var("E_src")
E_src.v()[:, :] = ymom.v() * grav
my_aux.fill_BC("E_src")
# ymom_xl[i,j] += 0.5*dt*dens[i-1,j]*grav
U_xl.v(buf=1, n=ivars.iymom)[:, :] += 0.5 * dt * ymom_src.ip(-1, buf=1)
U_xl.v(buf=1, n=ivars.iener)[:, :] += 0.5 * dt * E_src.ip(-1, buf=1)
# ymom_xr[i,j] += 0.5*dt*dens[i,j]*grav
U_xr.v(buf=1, n=ivars.iymom)[:, :] += 0.5 * dt * ymom_src.v(buf=1)
U_xr.v(buf=1, n=ivars.iener)[:, :] += 0.5 * dt * E_src.v(buf=1)
# ymom_yl[i,j] += 0.5*dt*dens[i,j-1]*grav
U_yl.v(buf=1, n=ivars.iymom)[:, :] += 0.5 * dt * ymom_src.jp(-1, buf=1)
U_yl.v(buf=1, n=ivars.iener)[:, :] += 0.5 * dt * E_src.jp(-1, buf=1)
# ymom_yr[i,j] += 0.5*dt*dens[i,j]*grav
U_yr.v(buf=1, n=ivars.iymom)[:, :] += 0.5 * dt * ymom_src.v(buf=1)
U_yr.v(buf=1, n=ivars.iener)[:, :] += 0.5 * dt * E_src.v(buf=1)
# =========================================================================
# compute transverse fluxes
# =========================================================================
tm_riem = tc.timer("riemann")
tm_riem.begin()
riemann = rp.get_param("compressible.riemann")
if riemann == "HLLC":
riemannFunc = ifc.riemann_hllc
elif riemann == "CGF":
riemannFunc = ifc.riemann_cgf
else:
msg.fail("ERROR: Riemann solver undefined")
_fx = riemannFunc(1, myg.qx, myg.qy, myg.ng, ivars.nvar, ivars.idens,
ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox,
ivars.naux, solid.xl, solid.xr, gamma, U_xl, U_xr)
_fy = riemannFunc(2, myg.qx, myg.qy, myg.ng, ivars.nvar, ivars.idens,
ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox,
ivars.naux, solid.yl, solid.yr, gamma, U_yl, U_yr)
F_x = ai.ArrayIndexer(d=_fx, grid=myg)
F_y = ai.ArrayIndexer(d=_fy, grid=myg)
tm_riem.end()
# =========================================================================
# construct the interface values of U now
# =========================================================================
"""
finally, we can construct the state perpendicular to the interface
by adding the central difference part to the trasverse flux
difference.
The states that we represent by indices i,j are shown below
(1,2,3,4):
j+3/2--+----------+----------+----------+
| | | |
| | | |
j+1 -+ | | |
| | | |
| | | | 1: U_xl[i,j,:] = U
j+1/2--+----------XXXXXXXXXXXX----------+ i-1/2,j,L
| X X |
| X X |
j -+ 1 X 2 X | 2: U_xr[i,j,:] = U
| X X | i-1/2,j,R
| X 4 X |
j-1/2--+----------XXXXXXXXXXXX----------+
| | 3 | | 3: U_yl[i,j,:] = U
| | | | i,j-1/2,L
j-1 -+ | | |
| | | |
| | | | 4: U_yr[i,j,:] = U
j-3/2--+----------+----------+----------+ i,j-1/2,R
| | | | | | |
i-1 i i+1
i-3/2 i-1/2 i+1/2 i+3/2
remember that the fluxes are stored on the left edge, so
F_x[i,j,:] = F_x
i-1/2, j
F_y[i,j,:] = F_y
i, j-1/2
"""
tm_transverse = tc.timer("transverse flux addition")
tm_transverse.begin()
dtdx = dt / myg.dx
dtdy = dt / myg.dy
b = (2, 1)
for n in range(ivars.nvar):
# U_xl[i,j,:] = U_xl[i,j,:] - 0.5*dt/dy * (F_y[i-1,j+1,:] - F_y[i-1,j,:])
U_xl.v(buf=b, n=n)[:, :] += \
- 0.5*dtdy*(F_y.ip_jp(-1, 1, buf=b, n=n) - F_y.ip(-1, buf=b, n=n))
# U_xr[i,j,:] = U_xr[i,j,:] - 0.5*dt/dy * (F_y[i,j+1,:] - F_y[i,j,:])
U_xr.v(buf=b, n=n)[:, :] += \
- 0.5*dtdy*(F_y.jp(1, buf=b, n=n) - F_y.v(buf=b, n=n))
# U_yl[i,j,:] = U_yl[i,j,:] - 0.5*dt/dx * (F_x[i+1,j-1,:] - F_x[i,j-1,:])
U_yl.v(buf=b, n=n)[:, :] += \
- 0.5*dtdx*(F_x.ip_jp(1, -1, buf=b, n=n) - F_x.jp(-1, buf=b, n=n))
# U_yr[i,j,:] = U_yr[i,j,:] - 0.5*dt/dx * (F_x[i+1,j,:] - F_x[i,j,:])
U_yr.v(buf=b, n=n)[:, :] += \
- 0.5*dtdx*(F_x.ip(1, buf=b, n=n) - F_x.v(buf=b, n=n))
tm_transverse.end()
# =========================================================================
# construct the fluxes normal to the interfaces
# =========================================================================
# up until now, F_x and F_y stored the transverse fluxes, now we
# overwrite with the fluxes normal to the interfaces
tm_riem.begin()
_fx = riemannFunc(1, myg.qx, myg.qy, myg.ng, ivars.nvar, ivars.idens,
ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox,
ivars.naux, solid.xl, solid.xr, gamma, U_xl, U_xr)
_fy = riemannFunc(2, myg.qx, myg.qy, myg.ng, ivars.nvar, ivars.idens,
ivars.ixmom, ivars.iymom, ivars.iener, ivars.irhox,
ivars.naux, solid.yl, solid.yr, gamma, U_yl, U_yr)
F_x = ai.ArrayIndexer(d=_fx, grid=myg)
F_y = ai.ArrayIndexer(d=_fy, grid=myg)
tm_riem.end()
# =========================================================================
# apply artificial viscosity
# =========================================================================
cvisc = rp.get_param("compressible.cvisc")
_ax, _ay = ifc.artificial_viscosity(myg.qx, myg.qy, myg.ng, myg.dx, myg.dy,
cvisc, q.v(n=ivars.iu, buf=myg.ng),
q.v(n=ivars.iv, buf=myg.ng))
avisco_x = ai.ArrayIndexer(d=_ax, grid=myg)
avisco_y = ai.ArrayIndexer(d=_ay, grid=myg)
b = (2, 1)
for n in range(ivars.nvar):
# F_x = F_x + avisco_x * (U(i-1,j) - U(i,j))
var = my_data.get_var_by_index(n)
F_x.v(buf=b, n=n)[:, :] += \
avisco_x.v(buf=b)*(var.ip(-1, buf=b) - var.v(buf=b))
# F_y = F_y + avisco_y * (U(i,j-1) - U(i,j))
F_y.v(buf=b, n=n)[:, :] += \
avisco_y.v(buf=b)*(var.jp(-1, buf=b) - var.v(buf=b))
tm_flux.end()
return F_x, F_y
def fluxes(myd, rp, ivars, solid, tc):
myg = myd.grid
gamma = rp.get_param("eos.gamma")
# get the cell-average data
U_avg = myd.data
# convert U from cell-centers to cell averages
U_cc = np.zeros_like(U_avg)
U_cc[:, :, ivars.idens] = myd.to_centers("density")
U_cc[:, :, ivars.ixmom] = myd.to_centers("x-momentum")
U_cc[:, :, ivars.iymom] = myd.to_centers("y-momentum")
U_cc[:, :, ivars.iener] = myd.to_centers("energy")
# compute the primitive variables of both the cell-center and averages
q_bar = comp.cons_to_prim(U_avg, gamma, ivars, myd.grid)
q_cc = comp.cons_to_prim(U_cc, gamma, ivars, myd.grid)
# compute the 4th-order approximation to the cell-average primitive state
q_avg = myg.scratch_array(nvar=ivars.nq)
for n in range(ivars.nq):
q_avg.v(n=n, buf=3)[:, :] = q_cc.v(
n=n, buf=3) + myg.dx**2 / 24.0 * q_bar.lap(n=n, buf=3)
# flattening -- there is a single flattening coefficient (xi) for all directions
use_flattening = rp.get_param("compressible.use_flattening")
if use_flattening:
xi_x = reconstruction.flatten(myg, q_bar, 1, ivars, rp)
xi_y = reconstruction.flatten(myg, q_bar, 2, ivars, rp)
xi = reconstruction.flatten_multid(myg, q_bar, xi_x, xi_y, ivars)
else:
xi = 1.0
fluxes = []
# for debugging
nolimit = 0
for idir in [1, 2]:
# interpolate <W> to faces (with limiting)
q_l = myg.scratch_array(nvar=ivars.nq)
q_r = myg.scratch_array(nvar=ivars.nq)
if nolimit:
for n in range(ivars.nq):
if idir == 1:
qtmp = 7./12.*(q_avg.ip(-1, n=n, buf=1) + q_avg.v(n=n, buf=1)) - \
1./12.*(q_avg.ip(-2, n=n, buf=1) + q_avg.ip(1, n=n, buf=1))
else:
qtmp = 7./12.*(q_avg.jp(-1, n=n, buf=1) + q_avg.v(n=n, buf=1)) - \
1./12.*(q_avg.jp(-2, n=n, buf=1) + q_avg.jp(1, n=n, buf=1))
q_l.v(n=n, buf=1)[:, :] = qtmp
q_r.v(n=n, buf=1)[:, :] = qtmp
else:
for n in range(ivars.nq):
q_l[:, :,
n], q_r[:, :,
n] = interface_f.states(q_avg[:, :, n], myg.qx,
myg.qy, myg.ng, idir)
# apply flattening
for n in range(ivars.nq):
if idir == 1:
q_l.ip(
1, n=n,
buf=2)[:, :] = xi.v(buf=2) * q_l.ip(1, n=n, buf=2) + (
1.0 - xi.v(buf=2)) * q_avg.v(n=n, buf=2)
q_r.v(n=n, buf=2)[:, :] = xi.v(buf=2) * q_r.v(
n=n, buf=2) + (1.0 - xi.v(buf=2)) * q_avg.v(n=n, buf=2)
else:
q_l.jp(
1, n=n,
buf=2)[:, :] = xi.v(buf=2) * q_l.jp(1, n=n, buf=2) + (
1.0 - xi.v(buf=2)) * q_avg.v(n=n, buf=2)
q_r.v(n=n, buf=2)[:, :] = xi.v(buf=2) * q_r.v(
n=n, buf=2) + (1.0 - xi.v(buf=2)) * q_avg.v(n=n, buf=2)
_q = cf.riemann_prim(idir, myg.qx, myg.qy, myg.ng, ivars.nq,
ivars.irho, ivars.iu, ivars.iv, ivars.ip,
ivars.ix, ivars.naux, 0, 0, gamma, q_l, q_r)
q_int_avg = ai.ArrayIndexer(_q, grid=myg)
# calculate the face-centered W using the transverse Laplacian
q_int_fc = myg.scratch_array(nvar=ivars.nq)
if idir == 1:
for n in range(ivars.nq):
q_int_fc.v(n=n, buf=myg.ng - 1)[:, :] = q_int_avg.v(
n=n, buf=myg.ng -
1) - 1.0 / 24.0 * (q_int_avg.jp(1, n=n, buf=myg.ng - 1) -
2 * q_int_avg.v(n=n, buf=myg.ng - 1) +
q_int_avg.jp(-1, n=n, buf=myg.ng - 1))
else:
for n in range(ivars.nq):
q_int_fc.v(n=n, buf=myg.ng - 1)[:, :] = q_int_avg.v(
n=n, buf=myg.ng -
1) - 1.0 / 24.0 * (q_int_avg.ip(1, n=n, buf=myg.ng - 1) -
2 * q_int_avg.v(n=n, buf=myg.ng - 1) +
q_int_avg.ip(-1, n=n, buf=myg.ng - 1))
# compute the final fluxes
F_fc = flux_cons(ivars, idir, gamma, q_int_fc)
F_avg = flux_cons(ivars, idir, gamma, q_int_avg)
if idir == 1:
F_x = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
F_x.v(n=n, buf=1)[:, :] = F_fc.v(n=n, buf=1) + 1.0 / 24.0 * (
F_avg.jp(1, n=n, buf=1) - 2 * F_avg.v(n=n, buf=1) +
F_avg.jp(-1, n=n, buf=1))
else:
F_y = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
F_y.v(n=n, buf=1)[:, :] = F_fc.v(n=n, buf=1) + 1.0 / 24.0 * (
F_avg.ip(1, n=n, buf=1) - 2 * F_avg.v(n=n, buf=1) +
F_avg.ip(-1, n=n, buf=1))
return F_x, F_y
def fluxes(myd, rp, ivars, solid, tc):
alpha = 0.3
beta = 0.3
myg = myd.grid
gamma = rp.get_param("eos.gamma")
# get the cell-average data
U_avg = myd.data
# convert U from cell-centers to cell averages
U_cc = np.zeros_like(U_avg)
U_cc[:, :, ivars.idens] = myd.to_centers("density")
U_cc[:, :, ivars.ixmom] = myd.to_centers("x-momentum")
U_cc[:, :, ivars.iymom] = myd.to_centers("y-momentum")
U_cc[:, :, ivars.iener] = myd.to_centers("energy")
# compute the primitive variables of both the cell-center and averages
q_bar = comp.cons_to_prim(U_avg, gamma, ivars, myd.grid)
q_cc = comp.cons_to_prim(U_cc, gamma, ivars, myd.grid)
# compute the 4th-order approximation to the cell-average primitive state
q_avg = myg.scratch_array(nvar=ivars.nq)
for n in range(ivars.nq):
q_avg.v(n=n, buf=3)[:, :] = q_cc.v(n=n, buf=3) + myg.dx**2/24.0 * q_bar.lap(n=n, buf=3)
# flattening -- there is a single flattening coefficient (xi) for all directions
use_flattening = rp.get_param("compressible.use_flattening")
if use_flattening:
xi_x = reconstruction.flatten(myg, q_bar, 1, ivars, rp)
xi_y = reconstruction.flatten(myg, q_bar, 2, ivars, rp)
xi = reconstruction.flatten_multid(myg, q_bar, xi_x, xi_y, ivars)
else:
xi = 1.0
# for debugging
nolimit = 0
for idir in [1, 2]:
# interpolate <W> to faces (with limiting)
q_l = myg.scratch_array(nvar=ivars.nq)
q_r = myg.scratch_array(nvar=ivars.nq)
if nolimit:
for n in range(ivars.nq):
if idir == 1:
qtmp = 7./12.*(q_avg.ip(-1, n=n, buf=1) + q_avg.v(n=n, buf=1)) - \
1./12.*(q_avg.ip(-2, n=n, buf=1) + q_avg.ip(1, n=n, buf=1))
else:
qtmp = 7./12.*(q_avg.jp(-1, n=n, buf=1) + q_avg.v(n=n, buf=1)) - \
1./12.*(q_avg.jp(-2, n=n, buf=1) + q_avg.jp(1, n=n, buf=1))
q_l.v(n=n, buf=1)[:, :] = qtmp
q_r.v(n=n, buf=1)[:, :] = qtmp
else:
for n in range(ivars.nq):
q_l[:, :, n], q_r[:, :, n] = interface.states(q_avg[:, :, n], myg.ng, idir)
# apply flattening
for n in range(ivars.nq):
if idir == 1:
q_l.ip(1, n=n, buf=2)[:, :] = xi.v(buf=2)*q_l.ip(1, n=n, buf=2) + \
(1.0 - xi.v(buf=2))*q_avg.v(n=n, buf=2)
q_r.v(n=n, buf=2)[:, :] = xi.v(buf=2)*q_r.v(n=n, buf=2) + \
(1.0 - xi.v(buf=2))*q_avg.v(n=n, buf=2)
else:
q_l.jp(1, n=n, buf=2)[:, :] = xi.v(buf=2)*q_l.jp(1, n=n, buf=2) + \
(1.0 - xi.v(buf=2))*q_avg.v(n=n, buf=2)
q_r.v(n=n, buf=2)[:, :] = xi.v(buf=2)*q_r.v(n=n, buf=2) + \
(1.0 - xi.v(buf=2))*q_avg.v(n=n, buf=2)
_q = cf.riemann_prim(idir, myg.ng,
ivars.irho, ivars.iu, ivars.iv, ivars.ip, ivars.ix, ivars.naux,
0, 0,
gamma, q_l, q_r)
q_int_avg = ai.ArrayIndexer(_q, grid=myg)
# calculate the face-centered W using the transverse Laplacian
q_int_fc = myg.scratch_array(nvar=ivars.nq)
if idir == 1:
for n in range(ivars.nq):
q_int_fc.v(n=n, buf=myg.ng-1)[:, :] = q_int_avg.v(n=n, buf=myg.ng-1) - \
1.0/24.0 * (q_int_avg.jp(1, n=n, buf=myg.ng-1) -
2*q_int_avg.v(n=n, buf=myg.ng-1) +
q_int_avg.jp(-1, n=n, buf=myg.ng-1))
else:
for n in range(ivars.nq):
q_int_fc.v(n=n, buf=myg.ng-1)[:, :] = q_int_avg.v(n=n, buf=myg.ng-1) - \
1.0/24.0 * (q_int_avg.ip(1, n=n, buf=myg.ng-1) -
2*q_int_avg.v(n=n, buf=myg.ng-1) +
q_int_avg.ip(-1, n=n, buf=myg.ng-1))
# compute the final fluxes
F_fc = flux_cons(ivars, idir, gamma, q_int_fc)
F_avg = flux_cons(ivars, idir, gamma, q_int_avg)
if idir == 1:
F_x = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
F_x.v(n=n, buf=1)[:, :] = F_fc.v(n=n, buf=1) + \
1.0/24.0 * (F_avg.jp(1, n=n, buf=1) -
2*F_avg.v(n=n, buf=1) +
F_avg.jp(-1, n=n, buf=1))
else:
F_y = myg.scratch_array(nvar=ivars.nvar)
for n in range(ivars.nvar):
F_y.v(n=n, buf=1)[:, :] = F_fc.v(n=n, buf=1) + \
1.0/24.0 * (F_avg.ip(1, n=n, buf=1) -
2*F_avg.v(n=n, buf=1) +
F_avg.ip(-1, n=n, buf=1))
# artificial viscosity McCorquodale & Colella Eq. 35, 36
# first find face-centered div
lam = myg.scratch_array()
if idir == 1:
lam.v(buf=1)[:, :] = (q_bar.v(buf=1, n=ivars.iu) -
q_bar.ip(-1, buf=1, n=ivars.iu))/myg.dx + \
0.25*(q_bar.jp(1, buf=1, n=ivars.iv) -
q_bar.jp(-1, buf=1, n=ivars.iv) +
q_bar.ip_jp(-1, 1, buf=1, n=ivars.iv) -
q_bar.ip_jp(-1, -1, buf=1, n=ivars.iv))/myg.dy
else:
lam.v(buf=1)[:, :] = (q_bar.v(buf=1, n=ivars.iv) -
q_bar.jp(-1, buf=1, n=ivars.iv))/myg.dy + \
0.25*(q_bar.ip(1, buf=1, n=ivars.iu) -
q_bar.ip(-1, buf=1, n=ivars.iu) +
q_bar.ip_jp(1, -1, buf=1, n=ivars.iu) -
q_bar.ip_jp(-1, -1, buf=1, n=ivars.iu))/myg.dx
test = myg.scratch_array()
test.v(buf=1)[:, :] = (myg.dx*lam.v(buf=1))**2 / \
(beta * gamma * q_bar.v(buf=1, n=ivars.ip) /
q_bar.v(buf=1, n=ivars.irho))
nu = myg.dx * lam * np.minimum(test, 1.0)
nu[lam >= 0.0] = 0.0
if idir == 1:
for n in range(ivars.nvar):
F_x.v(buf=1, n=n)[:, :] += alpha * nu.v(buf=1) * (U_avg.v(buf=1, n=n) - U_avg.ip(-1, buf=1, n=n))
else:
for n in range(ivars.nvar):
F_y.v(buf=1, n=n)[:, :] += alpha * nu.v(buf=1) * (U_avg.v(buf=1, n=n) - U_avg.jp(-1, buf=1, n=n))
return F_x, F_y
