pyna.toroidal.optimize.objectives#

pyna.toroidal.optimize.objectives — toroidal / stellarator optimisation objectives.

Toroidal ownership for scalar objective functions used in multi-objective stellarator optimisation.

Functions#

`neoclassical_epsilon_eff`(→ float)	Estimate effective ripple ε_eff (proxy for neoclassical transport).
`xpoint_field_parallelism`(→ float)	Measure field-line parallelism near X-points (for power exhaust).
`magnetic_axis_position`(→ Tuple[float, float])	Return `(R_axis, Z_axis)` of the magnetic axis.
`wall_clearance`(→ float)	Minimum distance from the LCFS to the first wall in the `(R, Z)` plane.
`compute_all_objectives`(→ Dict[str, float])	Compute all available physics objectives and return them as a dict.

pyna.toroidal.optimize.objectives.neoclassical_epsilon_eff(equilibrium, n_field_lines: int = 50, n_transits: int = 100) → float[source]#: Estimate effective ripple ε_eff (proxy for neoclassical transport).

pyna.toroidal.optimize.objectives.xpoint_field_parallelism(equilibrium, x_points: List[Tuple[float, float]], n_fieldlines: int = 20, n_transits: int = 30) → float[source]#: Measure field-line parallelism near X-points (for power exhaust).

pyna.toroidal.optimize.objectives.magnetic_axis_position(equilibrium) → Tuple[float, float][source]#: Return (R_axis, Z_axis) of the magnetic axis.

pyna.toroidal.optimize.objectives.wall_clearance(equilibrium, wall_R: numpy.ndarray, wall_Z: numpy.ndarray) → float[source]#: Minimum distance from the LCFS to the first wall in the (R, Z) plane.

pyna.toroidal.optimize.objectives.compute_all_objectives(equilibrium, wall_R: numpy.ndarray | None = None, wall_Z: numpy.ndarray | None = None, x_points: List[Tuple[float, float]] | None = None) → Dict[str, float][source]#: Compute all available physics objectives and return them as a dict.