bluemira.equilibria.optimisation.problem._minimal_current

Classes

MinimalCurrentCOP

Bounded, constrained, minimal current optimisation problem.

Module Contents

class bluemira.equilibria.optimisation.problem._minimal_current.MinimalCurrentCOP(eq: bluemira.equilibria.equilibrium.Equilibrium, max_currents: numpy.typing.ArrayLike | None = None, opt_algorithm: bluemira.optimisation.AlgorithmType = Algorithm.SLSQP, opt_conditions: dict[str, float | int] | None = None, opt_parameters: dict[str, float] | None = None, constraints: list[bluemira.equilibria.optimisation.constraints.UpdateableConstraint] | None = None, *, plot: bool | None = False, reference_eq: bluemira.equilibria.equilibrium.Equilibrium | None = None, diag_ops: bluemira.equilibria.diagnostics.EqDiagnosticOptions | None = None)

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

Bounded, constrained, minimal current optimisation problem.

Parameters:

• eq (bluemira.equilibria.equilibrium.Equilibrium) – Equilibrium object to optimise the currents for

• max_currents (numpy.typing.ArrayLike | None) – Current bounds vector [A]

• opt_conditions (dict[str, float | int] | None) – Optimiser conditions

• opt_algorithm (bluemira.optimisation.AlgorithmType) – optimiser algorithm

• opt_parameters (dict[str, float] | None) – Optimiser specific parameters, see https://nlopt.readthedocs.io/en/latest/NLopt_Reference/#algorithm-specific-parameters Otherwise, the parameters can be founded by digging through the source code.

• constraints (list[bluemira.equilibria.optimisation.constraints.UpdateableConstraint] | None) – List of optimisation constraints to apply to the optimisation problem

• plot (bool | None) – Whether or not to plot

• reference_eq (bluemira.equilibria.equilibrium.Equilibrium | None) – For plotting only. Equilibrium object to compare to current state of eq during optimisation. Will use initial state if None is chosen.

• diag_ops (bluemira.equilibria.diagnostics.EqDiagnosticOptions | None) – Diagnostic plotting options for Equilibrium

plotting_enabled = False

optimise(x0: numpy.typing.NDArray | None = None, *, fixed_coils: bool = True, keep_history: bool = False, check_constraints: bool = False) → bluemira.equilibria.optimisation.problem.base.CoilsetOptimiserResult

Run the optimisation problem

Parameters:

• fixed_coils (bool) – Whether or not to update to coilset response matrices

• x0 (numpy.typing.NDArray | None)

• keep_history (bool)

• check_constraints (bool)

Returns:

coilset – Optimised CoilSet

Return type:

CoilSet