Optimal Approximations of Coupling in Multidisciplinary Models
Name
optimal-decoupling-MDO-UQ.pdf
Description
Accepted version
Size
5.59 MB
Format
Adobe PDF
Checksum (MD5)
43c05e01ef69bc6c58f4277a81c495d1
Author(s)
Baptista, Ricardo Miguel
Marzouk, Youssef M
Willcox, Karen E
Date Issued
May 2018
Journal
AIAA journal
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Citation
Baptista, Ricardo et al. “Optimal Approximations of Coupling in Multidisciplinary Models.” AIAA journal, vol. 56, no. 6, 2018, pp. 2412-2428 © 2018 The Author(s)
Version
Author's final manuscript
Abstract
This paper presents a methodology for identifying important discipline couplings in multicomponent engineering systems. Coupling among disciplines contributes significantly to the computational cost of analyzinga system and can become particularly burdensome when coupled analyses are embedded with in 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. Typical practice derives such approximations in an ad hoc manner using expert opinion and domain experience. This work 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. An algorithm for selecting an optimal model is presented and illustrated in a fire-detection satellite model and a turbine engine cycle analysis model.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Center for Computational Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/1.J056888
