Optimal Approximations of Coupling in Multidisciplinary Models
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optimal-decoupling-MDO-UQ.pdf
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5.59 MB
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Author(s)
Peherstorfer, Benjamin
Baptista, Ricardo Miguel
Marzouk, Youssef M
Willcox, Karen E
Date Issued
January 2017
Journal
58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Citation
Baptista, Ricardo, et al. “Optimal Approximations of Coupling in Multidisciplinary Models.” 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 9-13 January 5, 2017, Grapevine, Texas, American Institute of Aeronautics and Astronautics (AIAA), 2017.
Version
Author's final manuscript
Abstract
Design of complex engineering systems requires coupled analyses of the multiple disciplines affecting system performance. The coupling among disciplines typically contributes significantly to the computational cost of analyzing the system, and can become particularly burdensome when coupled analyses are embedded within a design or optimization loop. In many cases, disciplines may be weakly coupled, so that some of the coupling or interaction terms can be neglected without significantly impacting the accuracy of the system output. However, typical practice derives such approximations in an ad hoc manner using expert opinion and domain experience. This paper proposes a new approach that formulates an optimization problem to find a model that optimally balances accuracy of the model outputs with the sparsity of the discipline couplings. An adaptive sequential Monte Carlo sampling-based technique is used to efficiently search the combinatorial model space of different discipline couplings. Finally, an algorithm for optimal model selection is presented and applied to identify the important discipline couplings in a fire detection satellite model and a turbine engine cycle analysis model.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2017-1935
