Application: Sobieski’s SuperSonic Business Jet (MDO)¶
This section describes how to setup and solve the MDO problem relative to the Sobieski test case with GEMSEO.
See also
To begin with a more simple MDO problem, and have a detailed description of how to plug a test case to GEMSEO, see Tutorial: How to solve a MDO problem.
Solving with a MDF formulation¶
In this example, we solve the range optimization using the following MDF formulation:
The MDF formulation couples all the disciplines during the Multi Disciplinary Analyses at each optimization iteration.
All the design variables are equally treated, concatenated in a single vector and given to a single optimization algorithm as the unknowns of the problem.
There is no specific constraint due to the MDF formulation.
Only the design constraints \(g\_1\), \(g\_2\) and \(g\_3\) are added to the problem.
The objective function is the range (the \(y\_4\) variable in the model), computed after the Multi Disciplinary Analyses.
Step 1: Creation of MDODiscipline
¶
To build the scenario, we first instantiate the disciplines. Here, the disciplines themselves have already been developed and interfaced with GEMSEO (see Benchmark problems).
from gemseo.api import create_discipline
disciplines = create_discipline(["SobieskiPropulsion", "SobieskiAerodynamics",
"SobieskiMission", "SobieskiStructure"])
Tip
For the disciplines that are not interfaced with GEMSEO, the GEMSEO’s api
eases the creation of disciplines without having to import them.
Step 2: Creation of Scenario
¶
The scenario delegates the creation of the optimization problem to the MDO formulation.
Therefore, it needs the list of disciplines
, the names of the formulation, the name of the objective function and the design space.
The
design_space
(here,design_space.txt
) defines the unknowns of the optimization problem, and their bounds. It contains all the design variables needed by the MDF formulation. They are selected among all the inputs of the disciplines, which can be listed withget_all_inputs()
.vi design_space.txt name lower_bound value upper_bound type x_shared 0.01 0.05 0.09 float x_shared 30000.0 45000.0 60000.0 float x_shared 1.4 1.6 1.8 float x_shared 2.5 5.5 8.5 float x_shared 40.0 55.0 70.0 float x_shared 500.0 1000.0 1500.0 float x_1 0.1 0.25 0.4 float x_1 0.75 1.0 1.25 float x_2 0.75 1.0 1.25 float x_3 0.1 0.5 1.0 float y_14 24850.0 50606.9741711 77100.0 float y_14 7700.0 7306.20262124 45000.0 float y_32 0.235 0.50279625 0.795 float y_31 2960.0 6354.32430691 10185.0 float y_24 0.44 4.15006276 11.13 float y_34 0.44 1.10754577 1.98 float y_23 3365.0 12194.2671934 26400.0 float y_21 24850.0 50606.9741711 77250.0 float y_12 24850.0 50606.9742 77250.0 float y_12 0.45 0.95 1.5 float
The available MDO formulations are located in the gemseo.formulations package, see Extending GEMSEO for extending GEMSEO with other formulations.
The
formulation
classname (here,"MDF"
) shall be passed to the scenario to select them.The list of available formulations can be obtained by using
get_available_formulations()
.>> from gemseo.api import get_available_formulations >> get_available_formulations() ['IDF', 'BiLevel', 'MDF', 'DisciplinaryOpt']
\(y\_4\) corresponds to the
objective_name
. This name must be one of the disciplines outputs, here the “SobieskiMission” discipline. The list of all outputs of the disciplines can be obtained by usingget_all_outputs()
:>> from gemseo.api import create_discipline, get_all_outputs >> disciplines = create_discipline(["SobieskiMission","SobieskiStructure","SobieskiPropulsion","SobieskiAerodynamics"]) >> get_all_outputs([disciplines]) ['y_4'] >> get_all_inputs([disciplines]) ['x_1', 'y_23', 'y_12', 'x_shared', 'x_3', 'y_14', 'y_31', 'x_2', 'y_24', 'y_32', 'y_34', 'y_21']
From these MDODiscipline
, design space filename, MDO formulation name and objective function name,
we build the scenario:
from gemseo.api import create_scenario
scenario = create_scenario(disciplines,
formulation="MDF",
maximize_objective=True,
objective_name="y_4",
design_space="design_space.txt")
The range function (\(y\_4\)) should be maximized. However, optimizers minimize functions by default. Then, when creating the scenario, the argument maximize_objective
shall be set to True
.
Scenario options¶
We may provide additional options to the scenario:
Function derivatives. As analytical disciplinary derivatives are available for Sobieski testcase, they can be used instead of computing the derivatives with finitedifferences or with the complexstep method:
scenario.optimizer.set_differentiation_method(“user”)
See also
The default behavior of the optimizer triggers finite differences. It corresponds to:
scenario.optimizer.set_differentiation_method(“finite_differences”,1e7)
See also
It it also possible to differentiate functions by means of the complex step method:
scenario.optimizer.set_differentiation_method(“complex_step”,1e30j)
Constraints¶
Similarly to the objective function, the constraints names are a subset of the disciplines
outputs, that can be obtained by using get_all_outputs()
.
The formulation has a powerful feature to automatically dispatch the constraints
(\(g\_1, g\_2, g\_3\)) and plug them to the optimizers depending on
the formulation. For that, we use the method gemseo.core.scenario.Scenario.add_constraint()
:
for constraint in [“g_1”, “g_2”, “g_3”]:
scenario.add_constraint(constraint, ’ineq’)
Step 3: Execution and visualization of the results¶
The algorithm options are provided as a dictionary to the execution
method of the scenario: algo_options = {'max_iter': 10, 'algo': "SLSQP"}
.
Warning
The mandatory options are the maximum number of iterations and the algorithm name.
The scenario is executed by means of the line:
scenario.execute(algo_options)
To visualize the optimization history:
scenario.post_process(“OptHistoryView”, save=True, show=False, file_path=“mdf”)
A whole variety of visualizations may be displayed for both MDO and DOE scenarios. These features are illustrated on the SSBJ use case in How to deal with postprocessing.
Influence of gradient computation method on performance¶
As mentioned in Coupled derivatives computation, several methods are available in order to perform the gradient computations: classical finite differences, complex step and Multi Disciplinary Analyses linearization in direct or adjoint mode. These modes are automatically selected by GEMSEO to minimize the CPU time. Yet, they can be forced on demand in each Multi Disciplinary Analyses:
from gemseo.core.jacobian_assembly import JacobianAssembly
scenario.formulation.mda.linearization_mode = JacobianAssembly.DIRECT_MODE
scenario.formulation.mda.matrix_type = JacobianAssembly.LINEAR_OPERATOR
The method used to solve the adjoint or direct linear problem may also be selected. GEMSEO can either assemble a sparse residual jacobian matrix of the Multi Disciplinary Analyses from the disciplines matrices. This has the advantage that LU factorizations may be stored to solve multiple right hand sides problems in a cheap way. But this requires extra memory.
scenario.formulation.mda.matrix_type = JacobianAssembly.SPARSE
scenario.formulation.mda.use_lu_fact = True
Altenatively, GEMSEO can implicitly create a matrixvector product operator, which is sufficient for GMRESlike solvers. It avoids to create an additional data structure. This can also be mandatory if the disciplines do not provide full Jacobian matrices but only matrixvector product operators.
scenario.formulation.mda.matrix_type = JacobianAssembly.LINEAR_OPERATOR
The next table shows the performance of each method for solving the Sobieski use case with MDF and IDF formulations. Efficiency of linearization is clearly visible has it takes from 10 to 20 times less CPU time to compute analytic derivatives of an Multi Disciplinary Analyses compared to finite difference and complex step. For IDF, improvements are less consequent, but direct linearization is more than 2.5 times faster than other methods.
Derivation Method 
Execution time (s) 


Finite differences 
8.22 
1.93 
Complex step 
18.11 
2.07 
Linearized (direct) 
0.90 
0.68 