Jacobi MDA

A smart MDA that solves only strongly coupled disciplines and then executes the weakly coupled ones

from __future__ import division, unicode_literals

from os import name as os_name

from gemseo.api import configure_logger, create_discipline, create_mda

IS_NT = os_name == "nt"

configure_logger()

Out:

<RootLogger root (INFO)>

Define a way to display results

def display_result(res, mda_name):
    """Display coupling and output variables in logger.

    @param res: result (dict) of MDA
    @param mda_name: name of the current MDA
    """
    # names of the coupling variables
    coupling_names = [
        "y_11",
        "y_12",
        "y_14",
        "y_21",
        "y_23",
        "y_24",
        "y_31",
        "y_32",
        "y_34",
    ]
    for coupling_var in coupling_names:
        print(
            "{}, coupling variable {}: {}".format(
                mda_name, coupling_var, res[coupling_var]
            ),
        )

    # names of the output variables
    output_names = ["y_1", "y_2", "y_3", "y_4", "g_1", "g_2", "g_3"]
    for output_var in output_names:
        print(
            "{}, output variable {}: {}".format(mda_name, output_var, res[output_var]),
        )

Create, execute and post-process MDA

We do not need to specify the inputs, the default inputs of the MDA will be used and computed from the Default inputs of the disciplines

disciplines = create_discipline(
    [
        "SobieskiStructure",
        "SobieskiPropulsion",
        "SobieskiAerodynamics",
        "SobieskiMission",
    ]
)
use_threading = False
if IS_NT:
    use_threading = True
mda = create_mda(
    "MDAJacobi",
    disciplines,
    acceleration="m2d",
    warm_start=True,
    n_processes=4,
    use_threading=use_threading,
)
res = mda.execute()
display_result(res, mda.name)
mda.plot_residual_history(
    n_iterations=10, logscale=[1e-8, 10.0], show=True, save=False, figsize=(10, 2)
)
MDAJacobi: residual plot

Out:

MDAJacobi, coupling variable y_11: [0.15591864]
MDAJacobi, coupling variable y_12: [5.06070636e+04 9.50000000e-01]
MDAJacobi, coupling variable y_14: [50607.06361137  7306.20262124]
MDAJacobi, coupling variable y_21: [50607.06361137]
MDAJacobi, coupling variable y_23: [12194.40917863]
MDAJacobi, coupling variable y_24: [4.15002177]
MDAJacobi, coupling variable y_31: [6354.40199354]
MDAJacobi, coupling variable y_32: [0.50280211]
MDAJacobi, coupling variable y_34: [1.10754577]
MDAJacobi, output variable y_1: [5.06070636e+04 7.30620262e+03 9.50000000e-01]
MDAJacobi, output variable y_2: [5.06070636e+04 1.21944092e+04 4.15002177e+00]
MDAJacobi, output variable y_3: [1.10754577e+00 6.35440199e+03 5.02802105e-01]
MDAJacobi, output variable y_4: [535.78213193]
MDAJacobi, output variable g_1: [ 0.035      -0.00666667 -0.0275     -0.04       -0.04833333 -0.09
 -0.15      ]
MDAJacobi, output variable g_2: [-0.04]
MDAJacobi, output variable g_3: [-0.99719789 -0.00280211  0.16206032 -0.02      ]

Total running time of the script: ( 0 minutes 1.023 seconds)

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