Optimal Approximations of Coupling in Multidisciplinary Models
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...
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American Institute of Aeronautics and Astronautics (AIAA)
2018
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| Online Access: | http://hdl.handle.net/1721.1/114613 https://orcid.org/0000-0002-0421-890X https://orcid.org/0000-0001-8242-3290 https://orcid.org/0000-0003-2156-9338 |
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| author | Peherstorfer, Benjamin Baptista, Ricardo Miguel Marzouk, Youssef M Willcox, Karen E |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Peherstorfer, Benjamin Baptista, Ricardo Miguel Marzouk, Youssef M Willcox, Karen E |
| author_sort | Peherstorfer, Benjamin |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:38:33Z |
| format | Article |
| id | mit-1721.1/114613 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:38:33Z |
| publishDate | 2018 |
| publisher | American Institute of Aeronautics and Astronautics (AIAA) |
| record_format | dspace |
| spelling | mit-1721.1/1146132022-10-01T10:14:31Z Optimal Approximations of Coupling in Multidisciplinary Models Peherstorfer, Benjamin Baptista, Ricardo Miguel Marzouk, Youssef M Willcox, Karen E Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Baptista, Ricardo Miguel Marzouk, Youssef M Willcox, Karen E 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. United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative (Award FA9550-15-1-0038) 2018-04-09T13:49:03Z 2018-04-09T13:49:03Z 2017-01 2018-04-04T15:16:28Z Article http://purl.org/eprint/type/ConferencePaper 978-1-62410-453-4 http://hdl.handle.net/1721.1/114613 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. https://orcid.org/0000-0002-0421-890X https://orcid.org/0000-0001-8242-3290 https://orcid.org/0000-0003-2156-9338 http://dx.doi.org/10.2514/6.2017-1935 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Institute of Aeronautics and Astronautics (AIAA) Other univ. web domain |
| spellingShingle | Peherstorfer, Benjamin Baptista, Ricardo Miguel Marzouk, Youssef M Willcox, Karen E Optimal Approximations of Coupling in Multidisciplinary Models |
| title | Optimal Approximations of Coupling in Multidisciplinary Models |
| title_full | Optimal Approximations of Coupling in Multidisciplinary Models |
| title_fullStr | Optimal Approximations of Coupling in Multidisciplinary Models |
| title_full_unstemmed | Optimal Approximations of Coupling in Multidisciplinary Models |
| title_short | Optimal Approximations of Coupling in Multidisciplinary Models |
| title_sort | optimal approximations of coupling in multidisciplinary models |
| url | http://hdl.handle.net/1721.1/114613 https://orcid.org/0000-0002-0421-890X https://orcid.org/0000-0001-8242-3290 https://orcid.org/0000-0003-2156-9338 |
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