bluemira.radiation_transport.radiation_profile

1-D radiation model inspired by the PROCESS function “plot_radprofile” in plot_proc.py.

Classes

`RadiationSourceParams`	Radiation source parameter frame
`Radiation`	Initial and generic class (no distinction between core and SOL)
`CoreRadiation`	Specific class to calculate the core radiation source.
`ScrapeOffLayerRadiation`	Specific class to calculate the SOL radiation source.
`DNScrapeOffLayerRadiation`	Specific class to make the SOL radiation source for a double null configuration.
`SNScrapeOffLayerRadiation`	Specific class to make the SOL radiation source for a double null configuration.
`RadiationSource`	Simplified solver to easily access the radiation model location inputs.

Module Contents

class bluemira.radiation_transport.radiation_profile.RadiationSourceParams

Bases: bluemira.base.parameter_frame.ParameterFrame

Radiation source parameter frame

sep_corrector_omp: bluemira.base.parameter_frame.Parameter[float]: Separation correction for double and single null plasma

sep_corrector_imp: bluemira.base.parameter_frame.Parameter[float]: Separation correction for double and single null plasma

alpha_n: bluemira.base.parameter_frame.Parameter[float]: Density profile factor

alpha_t: bluemira.base.parameter_frame.Parameter[float]: Temperature profile index

det_t: bluemira.base.parameter_frame.Parameter[float]: Detachment target temperature

eps_cool: bluemira.base.parameter_frame.Parameter[float]: electron energy loss

f_ion_t: bluemira.base.parameter_frame.Parameter[float]: Hydrogen first ionization

main_ext: bluemira.base.parameter_frame.Parameter[float]: radiation region extension in main chamber

rec_ext_out_leg: bluemira.base.parameter_frame.Parameter[float]: recycling region extension in outer leg

rec_ext_in_leg: bluemira.base.parameter_frame.Parameter[float]: recycling region extension in inner leg

fw_lambda_q_far_imp: bluemira.base.parameter_frame.Parameter[float]: Lambda_q far SOL imp

fw_lambda_q_far_omp: bluemira.base.parameter_frame.Parameter[float]: Lambda_q far SOL omp

fw_lambda_q_near_imp: bluemira.base.parameter_frame.Parameter[float]: Lambda_q near SOL imp

fw_lambda_q_near_omp: bluemira.base.parameter_frame.Parameter[float]: Lambda_q near SOL omp

lambda_t_factor: bluemira.base.parameter_frame.Parameter[float]: Lambda_t factor for non conduction-limited regime

lambda_n_factor: bluemira.base.parameter_frame.Parameter[float]: Lambda_n factor for non conduction-limited regime

gamma_sheath: bluemira.base.parameter_frame.Parameter[float]: sheath heat transmission coefficient

k_0: bluemira.base.parameter_frame.Parameter[float]: material’s conductivity

lfs_p_fraction: bluemira.base.parameter_frame.Parameter[float]: lfs fraction of SoL power

n_e_0: bluemira.base.parameter_frame.Parameter[float]: Electron density on axis

n_e_ped: bluemira.base.parameter_frame.Parameter[float]: Electron density pedestal height

n_e_sep: bluemira.base.parameter_frame.Parameter[float]: Electron density at separatrix

P_sep: bluemira.base.parameter_frame.Parameter[float]: Radiation power

rho_ped_n: bluemira.base.parameter_frame.Parameter[float]: Density pedestal r/a location

rho_ped_t: bluemira.base.parameter_frame.Parameter[float]: Temperature pedestal r/a location

n_points_core_95: bluemira.base.parameter_frame.Parameter[float]: rho discretization to 95% of core

n_points_core_99: bluemira.base.parameter_frame.Parameter[float]: rho discretization to 99% of core

n_points_mantle: bluemira.base.parameter_frame.Parameter[float]: rho discretization to separatrix

t_beta: bluemira.base.parameter_frame.Parameter[float]: Temperature profile index beta

T_e_0: bluemira.base.parameter_frame.Parameter[float]: Electron temperature on axis

T_e_ped: bluemira.base.parameter_frame.Parameter[float]: Electron temperature pedestal height

T_e_sep: bluemira.base.parameter_frame.Parameter[float]: Electron temperature at separatrix

theta_inner_target: bluemira.base.parameter_frame.Parameter[float]: Inner divertor poloidal angle with the separatrix flux line

theta_outer_target: bluemira.base.parameter_frame.Parameter[float]: Outer divertor poloidal angle with the separatrix flux line

class bluemira.radiation_transport.radiation_profile.Radiation(eq: bluemira.equilibria.equilibrium.Equilibrium, params: dict | bluemira.base.parameter_frame.ParameterFrame)

Initial and generic class (no distinction between core and SOL) to calculate radiation source within the flux tubes.

Parameters:

eq (bluemira.equilibria.equilibrium.Equilibrium)
params (dict | bluemira.base.parameter_frame.ParameterFrame)

params

eq

collect_flux_tubes(psi_n: numpy.ndarray) → list[bluemira.geometry.coordinates.Coordinates]

Collect flux tubes according to the normalised psi. For now only used for the core as for the SoL the flux surfaces to calculate the heat flux from charged particles are used.

Parameters:: psi_n (numpy.ndarray) – normalised psi
Returns:: list of flux tubes
Return type:: flux tubes

static flux_tube_pol_t(flux_tube: bluemira.geometry.coordinates.Coordinates, te_mp: float, t_rad_in: float = 0, t_rad_out: float = 0, rad_i: numpy.ndarray | None = None, rec_i: numpy.ndarray | None = None, t_tar: float = 0, *, core: bool = False, x_point_rad: bool = False, main_chamber_rad: bool = False) → numpy.ndarray

Along a single flux tube, it assigns different temperature values.

Parameters:

flux_tube (bluemira.geometry.coordinates.Coordinates) – flux tube geometry
te_mp (float) – electron temperature at the midplane [keV]
t_rad_in (float) – temperature value at the entrance of the radiation region [keV]
t_rad_out (float) – temperature value at the exit of the radiation region [keV]
rad_i (numpy.ndarray | None) – indexes of points, belonging to the flux tube, which fall into the radiation region
rec_i (numpy.ndarray | None) – indexes of points, belonging to the flux tube, which fall into the recycling region
t_tar (float) – electron temperature at the target [keV]
core (bool) – if True, t is constant along the flux tube. If false,it varies
x_point_rad (bool) – if True, it assumes there is no radiation at all in the recycling region.
main_chamber_rad (bool) – if True, the temperature from the midplane to the radiation region entrance is not constant

Returns:

poloidal distribution of electron temperature

Return type:

te [keV]

Raises:

ValueError – Required inputs not provided

static flux_tube_pol_n(flux_tube: bluemira.geometry.coordinates.Coordinates, ne_mp: float, n_rad_in: float | None = None, n_rad_out: float | None = None, rad_i: numpy.ndarray | None = None, rec_i: numpy.ndarray | None = None, n_tar: float | None = None, *, core: bool = False, main_chamber_rad: bool = False) → numpy.ndarray

Along a single flux tube, it assigns different density values.

Parameters:

flux_tube (bluemira.geometry.coordinates.Coordinates) – flux tube geometry
ne_mp (float) – electron density at the midplane [1/m^3]
n_rad_in (float | None) – density value at the entrance of the radiation region [1/m^3]
n_rad_out (float | None) – density value at the exit of the radiation region [1/m^3]
rad_i (numpy.ndarray | None) – indexes of points, belonging to the flux tube, which fall into the radiation region
rec_i (numpy.ndarray | None) – indexes of points, belonging to the flux tube, which fall into the recycling region
n_tar (float | None) – electron density at the target [1/m^3]
core (bool) – if True, n is constant along the flux tube. If false,it varies
main_chamber_rad (bool) – if True, the temperature from the midplane to the radiation region entrance is not constant

Returns:

poloidal distribution of electron density

Return type:

ne

Raises:

ValueError – Required inputs not provided

static mp_profile_plot(rho: numpy.ndarray, rad_power: numpy.ndarray, imp_name: str | list[str], ax=None) → matplotlib.pyplot.Axes

1D plot of the radiation power distribution along the midplane.

Parameters:

rho (numpy.ndarray) – dimensionless radius. Values between 0 and 1 for the plasma core. Values > 1 for the scrape-off layer
rad_power (numpy.ndarray) – radiated power at each mid-plane location corresponding to rho [Mw/m^3]
imp_name (str | list[str]) – impurity names

Returns:

axes on which the mid-plane radiation power distribution profile is plotted.

Return type:

ax

Notes

if rad_power.ndim > 1 the plot shows as many line as the number of impurities, plus the total radiated power

class bluemira.radiation_transport.radiation_profile.CoreRadiation(eq: bluemira.equilibria.equilibrium.Equilibrium, params: bluemira.base.parameter_frame.ParameterFrame, midplane_profiles: bluemira.radiation_transport.midplane_temperature_density.MidplaneProfiles, impurity_content: dict[str, float], impurity_data: dict[str, dict[str, tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]])

Bases: Radiation

Specific class to calculate the core radiation source.

Temperature and density are assumed to be constant along a single flux tube. In addition to Radiation, this class also includes the impurity data of all gases except Argon.

Parameters:

eq (bluemira.equilibria.equilibrium.Equilibrium) – The equilibrium defining flux surfaces.
midplane_profiles (bluemira.radiation_transport.midplane_temperature_density.MidplaneProfiles) – Electron density and electron temperature profile at the mid-plane.
impurity_content (dict[str, float]) – The dictionary of impurities (e.g. ‘H’) and their fractions (e.g. 1E-2) in the core.
impurity_data (dict[str, dict[str, tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]]) – The dictionary of impurities in the core at a defined time, sorted by species, then sorted by “T_ref” v.s. “L_ref”, where T_ref = reference ion temperature [eV], L_ref = the loss function value $L_z(n_e, T_e)$ [W m^3].
params (bluemira.base.parameter_frame.ParameterFrame)

impurities_content

imp_data_t_ref

imp_data_l_ref

imp_data_z_ref

impurity_symbols

profiles

calculate_mp_radiation_profile(): 1D profile of the line radiation loss at the mid-plane from the magnetic axis to the separatrix

plot_mp_radiation_profile(): Plot one dimensional behaviour of line radiation against the adimensional radius

calculate_core_distribution() → list[list[numpy.ndarray]]

Build poloidal distribution (distribution along the field lines) of line radiation loss in the plasma core.

Returns:: Line core radiation for each impurity species and for each closed flux line in the core.
Return type:: rad

calculate_core_radiation_map()

Build core radiation map.

Returns:: the core radiation map

radiation_distribution_plot(flux_tubes: numpy.ndarray, power_density: numpy.ndarray, ax=None) → matplotlib.pyplot.Axes

2D plot of the core radiation power distribution.

Parameters:

flux_tubes (numpy.ndarray) – array of the closed flux tubes within the separatrix.
power_density (numpy.ndarray) – arrays containing the power radiation density of the points lying on each flux tube [MW/m^3]

Returns:

the axes

Return type:

matplotlib.pyplot.Axes

plot_radiation_distribution(): Plot poloidal radiation distribution within the plasma core

plot_lz_vs_tref(): Plot radiative loss function for a set of given impurities against the reference temperature.

class bluemira.radiation_transport.radiation_profile.ScrapeOffLayerRadiation(eq: bluemira.equilibria.equilibrium.Equilibrium, params: bluemira.base.parameter_frame.ParameterFrame, x_sep_omp: float | None = None, x_sep_imp: float | None = None, dx_omp: float | None = None, dx_imp: float | None = None)

Bases: Radiation

Specific class to calculate the SOL radiation source. In the SOL is assumed a conduction dominated regime until the x-point, with no heat sinks, and a convection dominated regime between x-point and target.

Parameters:

eq (bluemira.equilibria.equilibrium.Equilibrium) – The equilibrium defining flux surfaces.
x_sep_omp (float | None) – outboard mid-plane separatrix x-coordinates [m]
x_sep_imp (float | None) – inboard mid-plane separatrix x-coordinates [m]
dx_omp (float | None) – The midplane spatial resolution between flux surfaces at the outboard [m]
dx_imp (float | None) – The midplane spatial resolution between flux surfaces at the inboard [m]
params (bluemira.base.parameter_frame.ParameterFrame)

x_sep_omp = None

x_sep_imp = None

dx_omp = None

dx_imp = None

collect_x_and_o_point_coordinates(): Magnetic axis coordinates and x-point(s) coordinates.

collect_separatrix_parameters(): Radiation source relevant parameters at the separatrix

x_point_radiation_z_ext(main_ext: float | None = None, pfr_ext: float = 0.3, *, low_div: bool = True) → tuple[float, Ellipsis]

Simple definition of a radiation region around the x-point. The region is supposed to extend from an arbitrary z coordinate on the main plasma side, to an arbitrary z coordinate on the private flux region side.

Parameters:

main_ext (float | None) – region extension on the main plasma side [m]
pfr_ext (float) – region extension on the private flux region side [m]
low_div (boolean) – default=True for the lower divertor. If False, upper divertor

Returns:

z_main – vertical (z coordinate) extension of the radiation region toward the main plasma [m]
z_pfr – vertical (z coordinate) extension of the radiation region toward the pfr [m]

Return type:

tuple[float, Ellipsis]

radiation_region_ends(z_main: float, z_pfr: float, *, lfs: bool = True) → tuple[float, Ellipsis]

Entering and exiting points (x, z) of the radiation region detected on the separatrix. The separatrix is supposed to be given by relevant half.

Parameters:

z_main (float) – vertical (z coordinate) extension of the radiation region toward the main plasma [m]
z_pfr (float) – vertical (z coordinate) extension of the radiation region toward the pfr [m]
lfs (bool) – default=True for the low field side (right half). If False, high field side (left half).

Returns:

entrance_x, entrance_z – x, z coordinates of the radiation region starting point
exit_x, exit_z – x, z coordinates of the radiation region ending point

Return type:

tuple[float, Ellipsis]

static radiation_region_points(flux_tube: bluemira.geometry.coordinates.Coordinates, z_main: float, z_pfr: float, *, lower: bool = True) → tuple[numpy.ndarray, Ellipsis]

For a given flux tube, indexes of points which fall respectively into the radiation and recycling region

Parameters:

flux_tube (bluemira.geometry.coordinates.Coordinates) – flux tube geometry
z_main (float) – vertical (z coordinate) extension of the radiation region toward the main plasma. Taken on the separatrix [m]
z_pfr (float) – vertical (z coordinate) extension of the radiation region toward the pfr. Taken on the separatrix [m]
lower (bool) – default=True for the lower divertor. If False, upper divertor

Returns:

rad_i – indexes of the points within the radiation region
rec_i – indexes pf the points within the recycling region

Return type:

tuple[numpy.ndarray, Ellipsis]

mp_electron_density_temperature_profiles(te_sep: float | None = None, *, omp: bool = True) → tuple[numpy.ndarray, Ellipsis]

Calculation of electron density and electron temperature profiles across the SoL at midplane. It uses the customised version for the mid-plane of the exponential decay law described in “electron_density_and_temperature_sol_decay”.

Parameters:

te_sep (float | None) – electron temperature at the separatrix [keV]
omp (bool) – outer mid-plane. Default value True. If False it stands for inner mid-plane

Returns:

te_sol – radial decayed temperatures through the SoL at the mid-plane. Unit [keV]
ne_sol – radial decayed densities through the SoL at the mid-plane. Unit [1/m^3]

Return type:

tuple[numpy.ndarray, Ellipsis]

any_point_density_temperature_profiles(x_p: float, z_p: float, t_p: float, t_u: float, *, lfs: bool = True) → tuple[numpy.ndarray, Ellipsis]

Calculation of electron density and electron temperature profiles across the SoL, starting from any point on the separatrix. (The z coordinate is the same. While the x coordinate changes) Using the equation to calculate T(s||).

Parameters:

x_p (float) – x coordinate of the point at the separatrix [m]
z_p (float) – z coordinate of the point at the separatrix [m]
t_p (float) – point temperature [eV]
t_u (float) – upstream temperature [eV]
lfs (bool) – low (toroidal) field side (outer wall side). Default value True. If False it stands for high field side (hfs).

Returns:

te_prof – radial decayed temperatures through the SoL. Unit [eV]
ne_prof – radial decayed densities through the SoL. Unit [1/m^3]

Return type:

tuple[numpy.ndarray, Ellipsis]

tar_electron_densitiy_temperature_profiles(ne_div: numpy.ndarray, te_div: numpy.ndarray, f_m: float = 1, *, detachment: bool = False) → tuple[numpy.ndarray, Ellipsis]

Calculation of electron density and electron temperature profiles across the SoL at the target. From the pressure balance, considering friction losses.

Parameters:

ne_div (numpy.ndarray) – density of the flux tubes at the entrance of the recycling region, assumed to be corresponding to the divertor plane [1/m^3]
te_div (numpy.ndarray) – temperature of the flux tubes at the entrance of the recycling region, assumed to be corresponding to the divertor plane [eV]
f_m (float) – fractional loss of pressure due to friction. It can vary between 0 and 1.
detachment (bool)

Returns:

te_t – target temperature [eV]
ne_t – target density [1/m^3]

Return type:

tuple[numpy.ndarray, Ellipsis]

calculate_sector_distributions(flux_tubes: numpy.ndarray, x_strike: float, z_strike: float, main_ext: float, firstwall_geom: bluemira.geometry.coordinates.Coordinates, pfr_ext: float | None = None, rec_ext: float | None = None, *, x_point_rad: bool = False, detachment: bool = False, lfs: bool = True, low_div: bool = True, main_chamber_rad: bool = False) → tuple[numpy.ndarray, Ellipsis]

Temperature and density profiles calculation. Within the scrape-off layer sector, it gives temperature and density profile along each flux tube.

Parameters:

flux_tubes (array) – set of flux tubes
x_strike (float) – x coordinate of the first open flux surface strike point [m]
z_strike (float) – z coordinate of the first open flux surface strike point [m]
main_ext (float) – extension of the radiation region from the x-point towards the main plasma [m]
firstwall_geom (bluemira.geometry.coordinates.Coordinates) – first wall geometry
pfr_ext (float | None) – extension of the radiation region from the x-point towards private flux region [m]
rec_ext (float | None) – extension of the recycling region, along the separatrix, from the target [m]
x_point_rad (bool) – if True, it assumes there is no radiation at all in the recycling region, and pfr_ext MUST be provided.
detachment (bool) – if True, it makes the temperature decay through the recycling region from the H ionization temperature to the assign temperature for detachment at the target. Else temperature is constant through the recycling region.
lfs (bool) – low field side. Default value True. If False it stands for high field side (hfs)
low_div (bool) – default=True for the lower divertor. If False, upper divertor
main_chamber_rad (bool) – if True, the temperature from the midplane to the radiation region entrance is not constant

Returns:

t_pol – temperature poloidal profile along each flux tube within the specified set [keV]
n_pol – density poloidal profile along each flux tube within the specified set [1/m^3]

Raises:

ValueError – Required inputs not provided

Return type:

tuple[numpy.ndarray, Ellipsis]

radiation_distribution_plot(flux_tubes, power_density, firstwall, ax=None)

2D plot of the radiation power distribution.

Parameters:

flux_tubes ([np.array]) – list of arrays of the flux tubes of different sectors. Example: if len(flux_tubes) = 2, it could contain the set of flux tubes of the lower that go from the omp to the outer lower divertor, and the set of flux tubes that go from the imp to the inner lower divertor.
power_density ([np.array]) – list of arrays containing the power radiation density of the points lying on the specified flux tubes. expected len(flux_tubes) = len(power_density)
firstwall (Grid) – first wall geometry

Returns:

Axes on which the 2D radiation power distribution is plotted.

Return type:

ax

static poloidal_distribution_plot(flux_tubes: numpy.ndarray, flux_property: numpy.ndarray, *, temperature: bool = True, ax=None)

2D plot of a generic property (density, temperature or radiation) as poloidal section the flux tube points.

Parameters:

flux_tubes (numpy.ndarray) – array of the open flux tubes within the SoL.
flux_property (numpy.ndarray) – arrays containing the property values associated to the points of each flux tube.
temperature (bool)

Returns:

Axes on which the flux tubes’ properties are plotted.

Return type:

ax

static plot_t_vs_n(flux_tube, t_distribution, n_distribution, ax1=None) → matplotlib.pyplot.Axes

2D plot of temperature and density of a single flux tube within the SoL

Parameters:

flux_tube (flux surface object as described in advective_transport.py) – open flux tube within the SoL.
t_distribution (np.array([np.array])) – arrays containing the temperature values associated to the points of the flux tube.
n_distribution (np.array([np.array])) – arrays containing the density values associated to the points of the flux tube.

Returns:

ax1 – Axes object on which the electron temperature is plotted
ax2 – Axes object on which the electron density is plotted

Return type:

matplotlib.pyplot.Axes

class bluemira.radiation_transport.radiation_profile.DNScrapeOffLayerRadiation(eq: bluemira.equilibria.equilibrium.Equilibrium, params: bluemira.base.parameter_frame.ParameterFrame, flux_surfaces: list[bluemira.equilibria.flux_surfaces.PartialOpenFluxSurface], impurity_content: dict[str, float], impurity_data: dict[str, dict[str, tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]], firstwall_geom: bluemira.geometry.coordinates.Coordinates, x_sep_omp: float | None = None, x_sep_imp: float | None = None, dx_omp: float | None = None, dx_imp: float | None = None)

Bases: ScrapeOffLayerRadiation

Specific class to make the SOL radiation source for a double null configuration. Here the SOL is divided into for regions. From the outer midplane to the outer lower target; from the omp to the outer upper target; from the inboard midplane to the inner lower target; from the imp to the inner upper target.

Parameters:

eq (bluemira.equilibria.equilibrium.Equilibrium) – The equilibrium defining flux surfaces.
flux_surfaces (list[bluemira.equilibria.flux_surfaces.PartialOpenFluxSurface]) – list of flux surfaces, all of which terminating at the first walls.
impurity_content (dict[str, float]) – The dictionary of impurities in the double-null’s scrape-off layer (e.g. ‘H’) and their fractions (e.g. 1E-2).
impurity_data (dict[str, dict[str, tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]]) – The dictionary of impurities in the double-null’s scrape-off layer at a defined time, sorted by species, then sorted by “T_ref” v.s. “L_ref”, where T_ref = reference ion temperature [eV], L_ref = the loss function value $L_z(n_e, T_e)$ [W m^3].
firstwall_geom (bluemira.geometry.coordinates.Coordinates) – The closed first wall geometry
x_sep_omp (float | None) – outboard mid-plane separatrix x-coordinates [m]
x_sep_imp (float | None) – inboard mid-plane separatrix x-coordinates [m]
dx_omp (float | None) – The midplane spatial resolution between flux surfaces at the outboard [m]
dx_imp (float | None) – The midplane spatial resolution between flux surfaces at the inboard [m]
params (bluemira.base.parameter_frame.ParameterFrame)

impurities_content

imp_data_t_ref

imp_data_l_ref

flux_tubes_lfs_low

flux_tubes_hfs_low

flux_tubes_lfs_up

flux_tubes_hfs_up

x_strike_lfs

z_strike_lfs

alpha_lfs

b_pol_out_tar

b_tor_out_tar

b_tot_out_tar

x_strike_hfs

z_strike_hfs

alpha_hfs

b_pol_inn_tar

b_tor_inn_tar

b_tot_inn_tar

t_omp

t_imp

calculate_sol_distribution(firstwall_geom: bluemira.geometry.coordinates.Coordinates)

Temperature and density profiles calculation. For each scrape-off layer sector, it gives temperature and density profile along each flux tube.

Parameters:

firstwall_geom (grid) – first wall geometry

Returns:

t_and_n_pol[“lfs_low”] – temperature and density poloidal profile along each flux tube within the lfs lower divertor set
t_and_n_pol[“lfs_up”] – temperature and density poloidal profile along each flux tube within the lfs upper divertor set
t_and_n_pol[“hfs_low”] – temperature and density poloidal profile along each flux tube within the hfs lower divertor set
t_and_n_pol[“hfs_up”] – temperature and density poloidal profile along each flux tube within the hfs upper divertor set

calculate_sol_radiation_distribution(lfs_low, lfs_up, hfs_low, hfs_up)

Radiation profiles calculation. For each scrape-off layer sector, it gives the radiation profile along each flux tube.

Parameters:

lfs_low – temperature and density poloidal profile along each flux tube within the lfs lower divertor set
lfs_up – temperature and density poloidal profile along each flux tube within the lfs upper divertor set
hfs_low – temperature and density poloidal profile along each flux tube within the hfs lower divertor set
hfs_up – temperature and density poloidal profile along each flux tube within the hfs upper divertor set

Returns:

rad[“lfs_low”] – radiation poloidal profile along each flux tube within the lfs lower divertor set
rad[“lfs_up”] – radiation poloidal profile along each flux tube within the lfs upper divertor set
rad[“hfs_low”] – radiation poloidal profile along each flux tube within the hfs lower divertor set
rad[“hfs_up”] – radiation poloidal profile along each flux tube within the hfs upper divertor set

calculate_sol_radiation_map(lfs_low, lfs_up, hfs_low, hfs_up)

Scrape off layer radiation map calculation.

Parameters:

lfs_low – radiation poloidal profile along each flux tube within the lfs lower divertor set
lfs_up – radiation poloidal profile along each flux tube within the lfs upper divertor set
hfs_low – radiation poloidal profile along each flux tube within the hfs lower divertor set
hfs_up – radiation poloidal profile along each flux tube within the hfs upper divertor set

Returns:

the SOL radiation map

plot_poloidal_radiation_distribution(firstwall_geom: bluemira.equilibria.grid.Grid)

Plot poloidal radiation distribution within the scrape-off layer

Parameters:: firstwall_geom (bluemira.equilibria.grid.Grid) – first wall geometry

class bluemira.radiation_transport.radiation_profile.SNScrapeOffLayerRadiation(eq: bluemira.equilibria.equilibrium.Equilibrium, params: bluemira.base.parameter_frame.ParameterFrame, flux_surfaces: list[bluemira.equilibria.flux_surfaces.PartialOpenFluxSurface], impurity_content: dict[str, float], impurity_data: dict[str, dict[str, tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]], firstwall_geom: bluemira.geometry.coordinates.Coordinates, x_sep_omp: float, dx_omp: float)

Bases: ScrapeOffLayerRadiation

Specific class to make the SOL radiation source for a double null configuration. Here the SOL is divided into for regions. From the outer midplane to the outer lower target; from the omp to the outer upper target; from the inboard midplane to the inner lower target; from the imp to the inner upper target.

Parameters:

eq (bluemira.equilibria.equilibrium.Equilibrium) – The equilibrium defining flux surfaces.
flux_surfaces (list[bluemira.equilibria.flux_surfaces.PartialOpenFluxSurface]) – list of flux surfaces, all of which terminating at the first walls.
impurity_content (dict[str, float]) – The dictionary of impurities in the single-null scrape-off layer (e.g. ‘H’) and their fractions (e.g. 1E-2).
impurity_data (dict[str, dict[str, tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]]) – The dictionary of impurities in the single-null scrape-off layer at a defined time, sorted by species, then sorted by “T_ref” v.s. “L_ref”, where T_ref = reference ion temperature [eV], L_ref = the loss function value $L_z(n_e, T_e)$ [W m^3].
firstwall_geom (bluemira.geometry.coordinates.Coordinates) – The closed first wall geometry
x_sep_omp (float) – outboard mid-plane separatrix x-coordinates [m]
dx_omp (float) – The midplane spatial resolution between flux surfaces at the outboard [m]
params (bluemira.base.parameter_frame.ParameterFrame)

impurities_content

imp_data_t_ref

imp_data_l_ref

flux_tubes_lfs

flux_tubes_hfs

x_strike_lfs

z_strike_lfs

alpha_lfs

b_pol_out_tar

b_tor_out_tar

b_tot_out_tar

x_strike_hfs

z_strike_hfs

alpha_hfs

b_pol_inn_tar

b_tor_inn_tar

b_tot_inn_tar

t_omp

calculate_sol_distribution(firstwall_geom: bluemira.equilibria.grid.Grid) → dict[str, numpy.typing.NDArray[numpy.float64]]

Temperature and density profiles calculation. For each scrape-off layer sector, it gives temperature and density profile along each flux tube.

Parameters:

firstwall_geom (bluemira.equilibria.grid.Grid) – first wall geometry

Returns:

t_and_n_pol[“lfs”] – temperature and density poloidal profile along each flux tube within the lfs divertor set
t_and_n_pol[“hfs”] – temperature and density poloidal profile along each flux tube within the hfs divertor set

Return type:

dict[str, numpy.typing.NDArray[numpy.float64]]

calculate_sol_radiation_distribution(lfs: numpy.ndarray, hfs: numpy.ndarray) → dict[str, list[list[numpy.ndarray]]]

Radiation profiles calculation. For each scrape-off layer sector, it gives the radiation profile along each flux tube.

Parameters:

lfs (numpy.ndarray) – temperature and density poloidal profile along each flux tube within the lfs divertor set
hfs (numpy.ndarray) – temperature and density poloidal profile along each flux tube within the hfs divertor set

Returns:

rad[“lfs”] – radiation poloidal profile along each flux tube within the lfs divertor set
rad[“hfs”] – radiation poloidal profile along each flux tube within the hfs divertor set

Return type:

dict[str, list[list[numpy.ndarray]]]

calculate_sol_radiation_map(lfs: numpy.ndarray, hfs: numpy.ndarray)

Scrape off layer radiation map calculation.

Parameters:

lfs (numpy.ndarray) – radiation poloidal profile along each flux tube within the lfs divertor set
hfs (numpy.ndarray) – radiation poloidal profile along each flux tube within the hfs upper divertor set

plot_poloidal_radiation_distribution(firstwall_geom: bluemira.geometry.coordinates.Coordinates)

Plot poloidal radiation distribution within the scrape-off layer

Parameters:: firstwall_geom (bluemira.geometry.coordinates.Coordinates) – first wall geometry

class bluemira.radiation_transport.radiation_profile.RadiationSource(eq: bluemira.equilibria.equilibrium.Equilibrium, firstwall_shape: bluemira.geometry.coordinates.Coordinates, params: bluemira.base.parameter_frame.typed.ParameterFrameLike, midplane_profiles: bluemira.radiation_transport.midplane_temperature_density.MidplaneProfiles, core_impurities: dict[str, float], sol_impurities: dict[str, float], confinement_time_core: float = np.inf, confinement_time_sol: float = 10, source_sol_dl: float | None = None, dx_mp: float = 0.001)

Simplified solver to easily access the radiation model location inputs.

Parameters:

eq (bluemira.equilibria.equilibrium.Equilibrium) – The equilibrium defining flux surfaces.
firstwall_shape (bluemira.geometry.coordinates.Coordinates) – BluemiraWire defining the first wall.
midplane_profiles (bluemira.radiation_transport.midplane_temperature_density.MidplaneProfiles) – Electron density and electron temperature profile at the mid-plane.
core_impurities (dict[str, float]) – The dictionary of impurities in the core (e.g. ‘H’) and their fractions (e.g. 1E-2).
sol_impurities (dict[str, float]) – The dictionary of impurities in the scrape-off layer (e.g. ‘H’) and their fractions (e.g. 1E-2).
confinement_time_sol (float) – Confinement timescale at the scrape-off layer [s]
confinement_time_core (float) – Confinement timescale in the core [s]
source_sol_dl (float | None) – the discretisation length of the scrape of layer source term [m]
dx_mp (float) – The midplane spatial resolution between flux surfaces [m]
params (bluemira.base.parameter_frame.typed.ParameterFrameLike)

params: RadiationSourceParams

param_cls: type[RadiationSourceParams]

eq

imp_content_core

imp_data_core

imp_content_sol

imp_data_sol

lcfs

midplane_profiles

core_rad = None

sol_rad = None

x_tot = None

z_tot = None

rad_tot = None

analyse(firstwall_geom: bluemira.geometry.coordinates.Coordinates) → tuple[CoreRadiation, ScrapeOffLayerRadiation]

Using core radiation model and SOL radiation model to calculate the radiation source at all points

Parameters:

firstwall_geom (bluemira.geometry.coordinates.Coordinates) – The closed first wall geometry

Returns:

self.core_rad – Core radiation source
self.sol_rad – Scrape-off-layer radiation source

Return type:

tuple[CoreRadiation, ScrapeOffLayerRadiation]

rad_core_by_psi_n(psi_n: float | numpy.ndarray) → numpy.ndarray

Calculation of core radiation source for a given (set of) psi norm value(s)

Parameters:: psi_n (float | numpy.ndarray) – The normalised magnetic flux value(s), between the minimum and maximum of [self.midplane_profiles.psi_n.min(), self.midplane_profiles.psi_n.max()].
Returns:: Local radiation source value(s) associated to the given psi_n
Return type:: rad_new

rad_core_by_points(x: float | numpy.ndarray, z: float | numpy.ndarray) → numpy.ndarray

Calculation of core radiation source for a given (set of) x, z coordinates

Parameters:

x (float | numpy.ndarray) – The x coordinate(s) of desired radiation source point(s)
z (float | numpy.ndarray) – The z coordinate(s) of desired radiation source point(s)

Returns:

Local radiation source value(s) associated to the point(s)

Return type:

self.rad_core_by_psi_n(psi_n)

rad_sol_by_psi_n(psi_n: float | numpy.ndarray[float]) → numpy.ndarray

Calculation of SOL radiation sources for a given psi norm value.

Each psi_n corresponds to a flux surface defined by a list of x-z coordinates. In the SOL (i.e. outside the LCFS) the radiation intensity is not constant, but rather varies across the flux surface. Therefore this method returns a radiation intensity value for each x-z coordinates in the list defining each flux surface.

Parameters:: psi_n (float) – The normalised magnetic flux value, between the minimum and maximum of [self.eq.psi_norm().min(), self.eq.psi_norm().max()].
Returns:: Local radiation source values associated to the given psi_n
Return type:: list

rad_sol_by_points(x_lst: collections.abc.Iterable[float], z_lst: collections.abc.Iterable[float]) → numpy.ndarray

Calculation of SOL radiation source for a given (set of) x, z coordinates

Parameters:

x_lst (collections.abc.Iterable[float]) – The x coordinate(s) of desired radiation source point(s)
z_lst (collections.abc.Iterable[float]) – The z coordinate(s) of desired radiation source point(s)

Returns:

Local radiation source value(s) associated to the point(s)

Return type:

list

rad_by_psi_n(psi_n: float) → numpy.ndarray

Calculation of any radiation source for a given (set of) psi norm value(s)

Parameters:: psi_n (float) – The normalised magnetic flux value(s), between the minimum and maximum of [self.eq.psi_norm().min(), self.eq.psi_norm().max()].
Returns:: Local radiation source value(s) associated to the given psi_n
Return type:: rad_any

rad_by_points(x: float | numpy.ndarray, z: float | numpy.ndarray) → numpy.ndarray

Calculation of any radiation source for a given (set of) x, z coordinates

Parameters:

x (float | numpy.ndarray) – The x coordinate(s) of desired radiation source point(s)
z (float | numpy.ndarray) – The z coordinate(s) of desired radiation source point(s)

Returns:

Local radiation source values located at the meshgrid formed by meshgrid of x and z.

Return type:

rad_any

rad_map(firstwall_geom: bluemira.geometry.coordinates.Coordinates)

Mapping all the radiation values associated to all the points as three arrays containing x coordinates, z coordinates and local radiated power density [MW/m^3]

Returns:

self.x_tot – x-coordinates of flux tubes [m]
self.z_tot – z-coordinates of flux tubes [m]
self.rad_tot – total radiated power density [MW/m^3]

Parameters:

firstwall_geom (bluemira.geometry.coordinates.Coordinates)

plot(ax=None, *, plot_flux_tubes: bool = False) → matplotlib.pyplot.Axes

Plot the RadiationSolver results.

Returns:: The axes object on which radiation solver results are plotted.
Return type:: ax
Parameters:: plot_flux_tubes (bool)