def first_order_update(CFL,
grid,
field,
rays,
system,
boundary_args=(),
source_args=()):
# this routine performs a first-order in time explicit update
# calculate particle velocities
call = np.array([timer()])
pu.get_par_acc(grid, field, rays, system)
call = np.append(timer())
pu.get_par_vel_terminal_velocity(grid, field, system)
call = np.append(timer())
pu.get_tgrow(grid, field, system)
call = np.append(timer())
# get timestep
dt = pu.get_timestep(CFL, grid, field)
call = np.append(timer())
# growth update
pu.size_update(grid, field, dt)
call = np.append(timer())
# do source update
source.update_source(grid, field, system, dt, source_args)
call = np.append(timer())
# now do advection update
pu.advection_update(grid, field, dt)
call = np.append(timer())
# boundary update
boundary.update_boundary(grid, field, boundary_args)
call = np.append(timer())
# now do diffusion update
pu.diffusion_update(grid, field, dt)
call = np.append(timer())
# boundary update
boundary.update_boundary(grid, field, boundary_args)
call = np.append(timer())
return dt, call
def first_order_update_semi_implict(dt,
grid,
field,
rays,
system,
boundary_args=(),
source_args=(),
getQ=None):
# this routine performs a first-order in time explicit update with semi-implicit update of particle
# velocity
# calculate particle velocities
pu.get_par_acc(grid, field, rays, system, getQ)
pu.get_velocity_semi_implicit(grid, field, system, dt)
pu.get_tgrow(grid, field, system)
# growth update
pu.size_update(grid, field, dt)
# do source update
source.update_source(grid, field, system, dt, source_args)
# now do advection update
pu.advection_update(grid, field, dt)
# boundary update
boundary.update_boundary(grid, field, boundary_args)
# now do diffusion update
pu.diffusion_update(grid, field, dt)
# boundary update
boundary.update_boundary(grid, field, boundary_args)
return dt
def first_order_update_semi_implict_numba(dt,
grid,
field,
rays,
system,
boundary_args=(),
source_args=(),
get_Q=None):
# this routine performs a first-order in time explicit update with a possible semi-implicit update of particle
# velocity - it includes parallel numba optimization for use on large grids
# the boundary update should be fast enough in pure numpy
# calculate particle velocities
pu.get_par_acc_jd(grid, field, rays, system, get_Q)
pu.get_velocity_semi_implicit_jd(grid, field, system, dt)
pu.get_tgrow_jd(grid, field, system)
# growth update
pu.size_update_jd(grid, field, dt)
# do source update
source.update_source_jd(grid, field, rays, system, dt, source_args)
# now do advection updates
if (field.short_friction):
### just density transport
pu.advection_update_jd(grid, field, dt)
else:
## Diffusive updated is included as velocity term here - velocities computed in size update
# as velocities have changed due to source update need to update boundries
boundary.update_boundary(grid, field, boundary_args)
## compute momenta
pu.compute_momenta_jd(grid, field)
## do density advenction
pu.advection_update_jd(grid, field, dt)
## do momentum advection
pu.momentum_advection_update_jd(grid, field, dt)
## recalculate velocities
pu.recalculate_velocities_jd(grid, field)
# boundary update
if (field.short_friction):
# do seperate diffusion update - otherwise included in advection as velocity
boundary.update_boundary(grid, field, boundary_args)
# now do diffusion update
pu.diffusion_update_jd(grid, field, dt)
# boundary update
boundary.update_boundary(grid, field, boundary_args)
return dt
ry.get_tau_grid_analytic(gd, sy)
#### Below is for testing ray-tracing scheme on analytic gas profile
#ry.do_ray_trace(fd.gas_dens*sy.kappa_star) ### using analytic gas optical depth calculation
#pu.update_tau_b_gas(fd,gd,ry,sy)
### Now initialise the initial conditions
fd.par_K[:] = Kzz
Sdot = Haze_flux
fd.par_dens[:, :, 0] = 1e-40
fd.gas_vth[1:-1, :] = 0.
bd.update_boundary(gd, fd)
kappa_bol = sy.kappa_star
Pstar = 1e-6 * 1e6
sigma_P = 0.5
a_init = 1e-7
# calculate optical depth for removal of haze production
get_tau_haze = InterpolatedUnivariateSpline(fd.gas_P[::-1, gd.NTH // 2 + 1],
ry.tau_b[::-1, gd.NTH // 2 + 1])
tau_haze = get_tau_haze(Pstar + 2. * sigma_P)
fd.par_size[:] = a_init
