asim.source_rad_prof.T)
ax.set_xlabel(r'$\rho_p$')
ax.set_ylabel('time [s]')
# set time-independent transport coefficients (flat D=1 m^2/s, V=-2 cm/s)
D_z = 1e4 * np.ones(len(asim.rvol_grid)) # cm^2/s
V_z = -2e2 * np.ones(len(asim.rvol_grid)) # cm/s
# run Aurora forward model and plot results
out = asim.run_aurora(D_z, V_z, plot=False)
nz_all = out[0] # impurity charge state densities are the first element of "out"
nz_init = nz_all[:,:,-1]
# calculate dilution cooling
rad = aurora.compute_rad(
imp, out[0].transpose(2,1,0), asim.ne, asim.Te, prad_flag=True)
tot_rad_dens = rad['tot'] # W/cm^3
line_rad_all = rad['line_rad'].T # W/cm^3
time_grid = copy.deepcopy(asim.time_grid)
# modify background temperature and density profiles based on tot_rad_dens
rhop_grid = asim.rhop_grid
Erad = np.trapz(tot_rad_dens[-1*n_rep:,:],axis=0,dx=dt) * 1.6e-13
Te_eV = radiation_cooling(rhop, rhop_grid, ne_cm3, nd_cm3, nz_init, Te_eV, Erad)
ne_cm3, Te_eV = dilution_cooling(rhop, rhop_grid, ne_cm3, nd_cm3, Te_eV, nz_init*0., nz_init)
kp['Te']['vals'] = Te_eV
kp['ne']['vals'] = ne_cm3
# update kinetic profile dependencies:
asim.setup_kin_profs_depts()
ne_cm3 = so.quants['ne'] *1e-6 #cm^-3
n0_cm3 = so.quants['nn'] *1e-6 #cm^-3
Te_eV = copy.deepcopy(so.quants['Te']) # eV
Te_eV[Te_eV<1.]= 1.0
filetypes=['acd','scd','ccd']
atom_data = aurora.get_atom_data(imp,filetypes)
logTe, fz = aurora.get_frac_abundances(
atom_data,ne_cm3,Te_eV, n0_by_ne=n0_cm3/ne_cm3, plot=False) # added n0
frac=0.01 # 1%
nz_cm3 = frac * ne_cm3[:,:,None] * fz # (time,nZ,space) --> (R,nZ,Z)
nz_cm3 = nz_cm3.transpose(0,2,1)
# compute radiation density from each grid point
out = aurora.compute_rad(imp,nz_cm3, ne_cm3, Te_eV, n0=n0_cm3, Ti=Te_eV,
prad_flag=True,thermal_cx_rad_flag=True)
# plot total radiated power
so.plot2d_b2(out['tot']*1e3, scale='log', label=r'$P_{rad}$ [$kW/m^3$]')
if device=='CMOD':
overplot_machine(shot, plt.gca()) # overplot machine tiles
# plot total line radiated power
so.plot2d_b2(out['line_rad'].sum(1)*1e3, scale='log', label=r'$P_{line,rad}$ [$kW/m^3$]')
if device=='CMOD':
overplot_machine(shot, plt.gca()) # overplot machine tiles
#compare_midplane_n0_with_expt(shot, rhop_LFS, neut_LFS*1e-6)
# check time grid:
_ = aurora.create_time_grid(namelist['timing'], plot=plot)
# set time-independent transport coefficients (flat D=1 m^2/s, V=-2 cm/s)
D_z = 1e4 * np.ones(len(asim.rvol_grid)) # cm^2/s
V_z = -2e2 * np.ones(len(asim.rvol_grid)) # cm/s
# run Aurora forward model and plot results
out = asim.run_aurora(D_z, V_z, plot=plot)
# extract densities and particle numbers in each simulation reservoir
nz, N_wall, N_div, N_pump, N_ret, N_tsu, N_dsu, N_dsul, rcld_rate, rclw_rate = out
# add radiation
asim.rad = aurora.compute_rad(imp, nz.transpose(2,1,0), asim.ne, asim.Te,
prad_flag=True, thermal_cx_rad_flag=False,
spectral_brem_flag=False, sxr_flag=False)
if plot:
# plot radiation profiles over radius and time
aurora.slider_plot(asim.rvol_grid, asim.time_out, asim.rad['line_rad'].transpose(1,2,0),
xlabel=r'$r_V$ [cm]', ylabel='time [s]', zlabel=r'Line radiation [$MW/m^3$]',
labels=[str(i) for i in np.arange(0,nz.shape[1])],
plot_sum=True, x_line=asim.rvol_lcfs)
# plot Delta-Zeff profiles over radius and time
asim.calc_Zeff()
if plot:
# plot variation of Zeff due to simulated impurity:
nz_cxr[:, cs, -1],
c=col,
ls='-',
label=f'{imp}{cs}+')
ax.plot([], [], 'w-', label=' ')
ax.plot([], [], 'k-', label='with CXR')
ax.plot([], [], 'k--', label='without CXR')
ax.legend(loc='best').set_draggable(True)
ax.set_xlabel(r'$\rho_p$')
ax.set_ylabel(r'$n_z$ [$cm^{-3}$]')
ax.set_xlim([0.85, np.max(asim.rhop_grid)])
# add radiation
asim.rad = aurora.compute_rad(imp,
nz.transpose(2, 1, 0),
asim.ne,
asim.Te,
prad_flag=True,
thermal_cx_rad_flag=False)
asim_cxr.rad = aurora.compute_rad(imp,
nz_cxr.transpose(2, 1, 0),
asim.ne,
asim.Te,
n0=asim_cxr.n0,
Ti=asim_cxr.Ti,
prad_flag=True,
thermal_cx_rad_flag=True)
c_cycle = aurora.get_color_cycle()
fig, ax = plt.subplots()
for cs in np.arange(nz.shape[1] -
1): # fully-stripped ions have no line radiation
# run Aurora forward model and plot results
out = asim.run_aurora(D_z, V_z, plot=True)
# extract densities and particle numbers in each simulation reservoir
nz, N_wall, N_div, N_pump, N_ret, N_tsu, N_dsu, N_dsul, rcld_rate, rclw_rate = out
# add radiation
asim.rad = aurora.compute_rad(
imp,
nz.transpose(2, 1, 0),
asim.ne,
asim.Te,
prad_flag=True,
thermal_cx_rad_flag=False,
spectral_brem_flag=False,
sxr_flag=True,
adas_files_sub={
'pls':
'/home/sciortino/atomlib/atomdat_master/pue2020_data/pls_Ca_9.dat',
'prs':
'/home/sciortino/atomlib/atomdat_master/pue2020_data/prs_Ca_9.dat'
})
# plot radiation profiles over radius and time
aurora.slider_plot(asim.rhop_grid,
asim.time_out,
asim.rad['sxr_line_rad'].transpose(1, 2, 0),
xlabel=r'$\rho_p$',
ylabel='time [s]',
zlabel=r'SXR Line radiation [$MW/m^3$]',
# ---------------------------
# Now, let's say we want to compute the radiated power by an impurity
# For simplicity, let's construct some charge state densities using fractional
# abundances at ionization equilibrium. Choose ion concentration (wrt to electrons):
frac = 1e-3
_Te, fz = aurora.atomic.get_frac_abundances(atom_data,
ne_cm3_2d,
Te_eV_2d,
plot=False)
# create some constant-fraction impurity charge state density spatial profiles
nz_cm3 = frac * ne_cm3_2d[:, None, :] * fz.transpose(0, 2, 1) # (R,nZ,Z)
# calculate radiation from chosen ion for these conditions
rad = aurora.compute_rad(
ion,
nz_cm3, # R,nZ,Z
ne_cm3_2d,
Te_eV_2d,
prad_flag=True)
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
cntr = axs[0].contourf(R, Z, nz_cm3[:, 16, :])
cbar = plt.colorbar(cntr, ax=axs[0])
cbar.set_label(fr'$n_{{{ion}17+}}$')
cntr = axs[1].contourf(R, Z, rad['line_rad'][:, 16, :])
cbar = plt.colorbar(cntr, ax=axs[1])
cbar.set_label(fr'$P_{{line,{ion}17+}}$ [MW/m$^3$]')
