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A global two-layer meta-model for response statistics in robust design optimization

Tanmoy Chatterjee, Michael Friswell, Sondipon Adhikari, Rajib Chowdhury

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...

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Published in: Engineering Optimization
ISSN: 0305-215X 1029-0273
Published: Informa UK Limited 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56123
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spelling 2021-12-06T15:43:01.5212545 v2 56123 2021-01-25 A global two-layer meta-model for response statistics in robust design optimization 5e637da3a34c6e97e2b744c2120db04d Tanmoy Chatterjee Tanmoy Chatterjee true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false cb6c378733c1f732411646825fb9e289 Rajib Chowdhury Rajib Chowdhury true false 2021-01-25 FGSEN 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. Journal Article Engineering Optimization 54 (2022) 1 1 17 Informa UK Limited 0305-215X 1029-0273 RDO; Kriging; HDMR; compressive sensing; adaptive sparse 11 1 2021 2021-01-11 10.1080/0305215x.2020.1861262 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2021-12-06T15:43:01.5212545 2021-01-25T11:21:27.6095702 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Tanmoy Chatterjee 1 Michael Friswell 2 Sondipon Adhikari 3 Rajib Chowdhury 4 56123__19331__1de4a069428043f8bd179bc86ae39a19.pdf 56123.pdf 2021-02-18T16:00:00.5196102 Output 623873 application/pdf Accepted Manuscript true 2022-01-11T00:00:00.0000000 true eng
title A global two-layer meta-model for response statistics in robust design optimization
spellingShingle A global two-layer meta-model for response statistics in robust design optimization
Tanmoy Chatterjee
Michael Friswell
Sondipon Adhikari
Rajib Chowdhury
title_short A global two-layer meta-model for response statistics in robust design optimization
title_full A global two-layer meta-model for response statistics in robust design optimization
title_fullStr A global two-layer meta-model for response statistics in robust design optimization
title_full_unstemmed A global two-layer meta-model for response statistics in robust design optimization
title_sort A global two-layer meta-model for response statistics in robust design optimization
author_id_str_mv 5e637da3a34c6e97e2b744c2120db04d
5894777b8f9c6e64bde3568d68078d40
4ea84d67c4e414f5ccbd7593a40f04d3
cb6c378733c1f732411646825fb9e289
author_id_fullname_str_mv 5e637da3a34c6e97e2b744c2120db04d_***_Tanmoy Chatterjee
5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari
cb6c378733c1f732411646825fb9e289_***_Rajib Chowdhury
author Tanmoy Chatterjee
Michael Friswell
Sondipon Adhikari
Rajib Chowdhury
author2 Tanmoy Chatterjee
Michael Friswell
Sondipon Adhikari
Rajib Chowdhury
format Journal article
container_title Engineering Optimization
container_volume 54 (2022)
container_issue 1
container_start_page 1
publishDate 2021
institution Swansea University
issn 0305-215X
1029-0273
doi_str_mv 10.1080/0305215x.2020.1861262
publisher Informa UK Limited
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
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description 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.
published_date 2021-01-11T04:10:50Z
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