bluemira.equilibria.optimisation.problem._breakdown

Classes

`BreakdownZoneStrategy`	Abstract base class for the definition of a breakdown zone strategy.
`CircularZoneStrategy`	Circular breakdown zone strategy.
`InboardBreakdownZoneStrategy`	Inboard breakdown zone strategy.
`OutboardBreakdownZoneStrategy`	Outboard breakdown zone strategy.
`InputBreakdownZoneStrategy`	User input breakdown zone strategy.
`BreakdownCOP`	Coilset optimisation problem for the premagnetisation / breakdown phase.

Module Contents

class bluemira.equilibria.optimisation.problem._breakdown.BreakdownZoneStrategy(R_0, A, tk_sol, **kwargs)

Bases: abc.ABC

Abstract base class for the definition of a breakdown zone strategy.

Parameters:

R_0 – Major radius of the reference plasma
A – Aspect ratio of the reference plasma
tk_sol – Thickness of the scrape-off layer

R_0

A

tk_sol

property breakdown_point: tuple[float, float]

Abstractmethod:
Return type:: tuple[float, float]

The location of the breakdown point.

Returns:

x_c – Radial coordinate of the breakdown point
z_c – Vertical coordinate of the breakdown point

Return type:

tuple[float, float]

property breakdown_radius: float

Abstractmethod:
Return type:: float

The radius of the breakdown zone.

abstract calculate_zone_points(n_points: int) → tuple[numpy.ndarray, numpy.ndarray]

Calculate the discretised set of points representing the breakdown zone.

Parameters:: n_points (int)
Return type:: tuple[numpy.ndarray, numpy.ndarray]

class bluemira.equilibria.optimisation.problem._breakdown.CircularZoneStrategy(R_0, A, tk_sol, **kwargs)

Bases: BreakdownZoneStrategy

Circular breakdown zone strategy.

calculate_zone_points(n_points: int) → tuple[numpy.ndarray, numpy.ndarray]

Calculate the discretised set of points representing the breakdown zone.

Returns:

x – The x coordinates of the zone
z – The z coordinates of the zone

Parameters:

n_points (int)

Return type:

tuple[numpy.ndarray, numpy.ndarray]

class bluemira.equilibria.optimisation.problem._breakdown.InboardBreakdownZoneStrategy(R_0, A, tk_sol, **kwargs)

Bases: CircularZoneStrategy

Inboard breakdown zone strategy.

property breakdown_point: tuple[float, float]

The location of the breakdown point.

Returns:

x_c – Radial coordinate of the breakdown point
z_c – Vertical coordinate of the breakdown point

Return type:

tuple[float, float]

property breakdown_radius: float

The radius of the breakdown zone.

Return type:: float

class bluemira.equilibria.optimisation.problem._breakdown.OutboardBreakdownZoneStrategy(R_0, A, tk_sol, **kwargs)

Bases: CircularZoneStrategy

Outboard breakdown zone strategy.

property breakdown_point: tuple[float, float]

The location of the breakdown point.

Returns:

x_c – Radial coordinate of the breakdown point
z_c – Vertical coordinate of the breakdown point

Return type:

tuple[float, float]

property breakdown_radius: float

The radius of the breakdown zone.

Return type:: float

class bluemira.equilibria.optimisation.problem._breakdown.InputBreakdownZoneStrategy(x_c, z_c, r_c)

Bases: CircularZoneStrategy

User input breakdown zone strategy.

x_c

z_c

r_c

property breakdown_point: tuple[float, float]

The location of the breakdown point.

Returns:

x_c – Radial coordinate of the breakdown point
z_c – Vertical coordinate of the breakdown point

Return type:

tuple[float, float]

property breakdown_radius: float

The radius of the breakdown zone.

Return type:: float

class bluemira.equilibria.optimisation.problem._breakdown.BreakdownCOP(breakdown: bluemira.equilibria.equilibrium.Breakdown, breakdown_strategy: BreakdownZoneStrategy, B_stray_max: float, B_stray_con_tol: float, n_B_stray_points: int, max_currents: numpy.typing.ArrayLike | None = None, opt_algorithm: bluemira.optimisation.AlgorithmType = Algorithm.SLSQP, opt_conditions: dict[str, float | int] | None = None, constraints: list[bluemira.equilibria.optimisation.constraints.UpdateableConstraint] | None = None)

Bases: bluemira.equilibria.optimisation.problem.base.EqCoilsetOptimisationProblem

Coilset optimisation problem for the premagnetisation / breakdown phase.

Parameters:

breakdown (bluemira.equilibria.equilibrium.Breakdown)
breakdown_strategy (BreakdownZoneStrategy)
B_stray_max (float)
B_stray_con_tol (float)
n_B_stray_points (int)
max_currents (numpy.typing.ArrayLike | None)
opt_algorithm (bluemira.optimisation.AlgorithmType)
opt_conditions (dict[str, float | int] | None)
constraints (list[bluemira.equilibria.optimisation.constraints.UpdateableConstraint] | None)

_args

optimise(x0=None, *, fixed_coils: bool = True, keep_history: bool = False, check_constraints: bool = False, verbose: bool = False)

Solve the optimisation problem.

Parameters:

fixed_coils (bool)
keep_history (bool)
check_constraints (bool)
verbose (bool)