Journal article 622 views 75 downloads
A global two-layer meta-model for response statistics in robust design optimization
Engineering Optimization, Volume: 54 (2022), Issue: 1, Pages: 1 - 17
Swansea University Authors: Tanmoy Chatterjee, Michael Friswell, Sondipon Adhikari, Rajib Chowdhury
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DOI (Published version): 10.1080/0305215x.2020.1861262
Abstract
Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conventional meta-model assisted RDO frameworks. The primary objective of this article is to minimize further...
| Published in: | Engineering Optimization |
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| ISSN: | 0305-215X 1029-0273 |
| Published: |
Informa UK Limited
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa56123 |
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| Abstract: |
Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conventional meta-model assisted RDO frameworks. The primary objective of this article is to minimize further the computational requirements of meta-model assisted RDO by developing a global two-layered approximation based RDO technique. The meta-model in the inner layer approximates the response quantity and the meta-model in the outer layer approximates the response statistics computed from the response meta-model. This approach eliminates both model building and Monte Carlo simulation from the optimization cycle, and requires considerably fewer actual response evaluations than a single-layered approximation. To demonstrate the approach, two recently developed compressive sensing enabled globally refined Kriging models have been utilized. The proposed framework is applied to one test example and two real-life applications to illustrate clearly its potential to yield robust optimal solutions with minimal computational cost. |
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| Keywords: |
RDO; Kriging; HDMR; compressive sensing; adaptive sparse |
| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
1 |
| End Page: |
17 |