mdo_scenario module¶
A scenario whose driver is an optimization algorithm.
- class gemseo.core.mdo_scenario.MDOScenario(disciplines, formulation, objective_name, design_space, name=None, grammar_type=GrammarType.JSON, maximize_objective=False, **formulation_options)[source]
Bases:
ScenarioA multidisciplinary scenario to be executed by an optimizer.
an
MDOScenariois a particularScenariowhose driver is an optimization algorithm. This algorithm must be implemented in anOptimizationLibrary.Initialize self. See help(type(self)) for accurate signature.
- Parameters:
disciplines (Sequence[MDODiscipline]) – The disciplines used to compute the objective, constraints and observables from the design variables.
formulation (str) – The class name of the
MDOFormulation, e.g."MDF","IDF"or"BiLevel".objective_name (str | Sequence[str]) – The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.
design_space (DesignSpace) – The search space including at least the design variables (some formulations requires additional variables, e.g.
IDFwith the coupling variables).name (str | None) – The name to be given to this scenario. If
None, use the name of the class.grammar_type (MDODiscipline.GrammarType) –
The grammar for the scenario and the MDO formulation.
By default it is set to “JSONGrammar”.
maximize_objective (bool) –
Whether to maximize the objective.
By default it is set to False.
**formulation_options (Any) – The options of the
MDOFormulation.
- MAX_ITER = 'max_iter'
- X_OPT = 'x_opt'
- cache: AbstractCache | None
The cache containing one or several executions of the discipline according to the cache policy.
- clear_history_before_run: bool
If
True, clear history before run.
- data_processor: DataProcessor
A tool to pre- and post-process discipline data.
- exec_for_lin: bool
Whether the last execution was due to a linearization.
- formulation: MDOFormulation
The MDO formulation.
- formulation_name: str
The name of the MDO formulation.
- input_grammar: BaseGrammar
The input grammar.
- jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]
The Jacobians of the outputs wrt inputs.
The structure is
{output: {input: matrix}}.
- name: str
The name of the discipline.
- optimization_result: OptimizationResult | None
The optimization result if the scenario has been executed; otherwise
None.
- output_grammar: BaseGrammar
The output grammar.
- re_exec_policy: ReExecutionPolicy
The policy to re-execute the same discipline.
- residual_variables: dict[str, str]
The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.
- run_solves_residuals: bool
Whether the run method shall solve the residuals.
Examples using MDOScenario¶
Example for exterior penalty applied to the Sobieski test case.
Pareto front on the Binh and Korn problem using a BiLevel formulation
Application: Sobieski’s Super-Sonic Business Jet (MDO)
MDO formulations for a toy example in aerostructure
Solve a 2D short cantilever topology optimization problem
