fastga_he.models.cost.lcc_learning_curve_discount module

class fastga_he.models.cost.lcc_learning_curve_discount.LCCLearningCurveDiscount(**kwargs)[source]

Bases: ExplicitComponent

Computation of the aircraft production learning curve discount factor for tooling and manufacturing. The computation is obtained from http://www.ae.metu.edu.tr/~ae452sc2/lecture8_cost.pdf. The learning curve percentage falls between 80% to 90% based on the results from [Bon17].

Store some bound methods so we can detect runtime overrides.

setup()[source]

Declare inputs and outputs.

Available attributes:

name pathname comm options

compute(inputs, outputs, discrete_inputs=None, discrete_outputs=None)[source]

Compute outputs given inputs. The model is assumed to be in an unscaled state.

An inherited component may choose to either override this function or to define a compute_primal function.

Parameters:

  • inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].

  • outputs (Vector) – Unscaled, dimensional output variables read via outputs[key].

  • discrete_inputs (dict-like or None) – If not None, dict-like object containing discrete input values.

  • discrete_outputs (dict-like or None) – If not None, dict-like object containing discrete output values.

compute_partials(inputs, partials, discrete_inputs=None)[source]

Compute sub-jacobian parts. The model is assumed to be in an unscaled state.

Parameters:

  • inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].

  • partials (Jacobian) – Sub-jac components written to partials[output_name, input_name]..

  • discrete_inputs (dict or None) – If not None, dict containing discrete input values.