Source code for torax.sources.gas_puff_source

# Copyright 2024 DeepMind Technologies Limited
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Gas puff source for the ne equation."""
import dataclasses
from typing import ClassVar, Literal

import chex
from torax import array_typing
from torax import state
from torax.config import runtime_params_slice
from torax.geometry import geometry
from torax.sources import base
from torax.sources import formulas
from torax.sources import runtime_params as runtime_params_lib
from torax.sources import source
from torax.sources import source_profiles
from torax.torax_pydantic import torax_pydantic


# Default value for the model function to be used for the gas puff
# source. This is also used as an identifier for the model function in
# the default source config for Pydantic to "discriminate" against.
DEFAULT_MODEL_FUNCTION_NAME: str = 'calc_puff_source'


# pylint: disable=invalid-name
[docs] @chex.dataclass(frozen=True) class DynamicGasPuffRuntimeParams(runtime_params_lib.DynamicRuntimeParams): puff_decay_length: array_typing.ScalarFloat S_puff_tot: array_typing.ScalarFloat
# Default formula: exponential with nref normalization.
[docs] def calc_puff_source( unused_static_runtime_params_slice: runtime_params_slice.StaticRuntimeParamsSlice, dynamic_runtime_params_slice: runtime_params_slice.DynamicRuntimeParamsSlice, geo: geometry.Geometry, source_name: str, unused_state: state.CoreProfiles, unused_calculated_source_profiles: source_profiles.SourceProfiles | None, ) -> tuple[chex.Array, ...]: """Calculates external source term for n from puffs.""" dynamic_source_runtime_params = dynamic_runtime_params_slice.sources[ source_name ] assert isinstance(dynamic_source_runtime_params, DynamicGasPuffRuntimeParams) return (formulas.exponential_profile( decay_start=1.0, width=dynamic_source_runtime_params.puff_decay_length, total=( dynamic_source_runtime_params.S_puff_tot / dynamic_runtime_params_slice.numerics.nref ), geo=geo, ),)
[docs] @dataclasses.dataclass(kw_only=True, frozen=True, eq=True) class GasPuffSource(source.Source): """Gas puff source for the ne equation.""" SOURCE_NAME: ClassVar[str] = 'gas_puff_source' model_func: source.SourceProfileFunction = calc_puff_source @property def source_name(self) -> str: return self.SOURCE_NAME @property def affected_core_profiles(self) -> tuple[source.AffectedCoreProfile, ...]: return (source.AffectedCoreProfile.NE,)
[docs] class GasPuffSourceConfig(base.SourceModelBase): """Gas puff source for the ne equation. Attributes: source_name: Name of the source, hardcoded to 'gas_puff_source' puff_decay_length: exponential decay length of gas puff ionization [normalized radial coord] S_puff_tot: total gas puff particles/s """ model_function_name: Literal['calc_puff_source'] = 'calc_puff_source' puff_decay_length: torax_pydantic.TimeVaryingScalar = ( torax_pydantic.ValidatedDefault(0.05) ) S_puff_tot: torax_pydantic.TimeVaryingScalar = ( torax_pydantic.ValidatedDefault(1e22) ) mode: runtime_params_lib.Mode = runtime_params_lib.Mode.MODEL_BASED @property def model_func(self) -> source.SourceProfileFunction: return calc_puff_source
[docs] def build_dynamic_params( self, t: chex.Numeric, ) -> DynamicGasPuffRuntimeParams: return DynamicGasPuffRuntimeParams( prescribed_values=tuple( [v.get_value(t) for v in self.prescribed_values] ), puff_decay_length=self.puff_decay_length.get_value(t), S_puff_tot=self.S_puff_tot.get_value(t), )
[docs] def build_source(self) -> GasPuffSource: return GasPuffSource(model_func=self.model_func)