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Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space

A. Kundu, H.G. Matthies, M.I. Friswell, Michael Friswell

Computer Methods in Applied Mechanics and Engineering, Volume: 337, Pages: 281 - 304

Swansea University Author: Michael Friswell

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DOI (Published version): 10.1016/j.cma.2018.03.041

Abstract

A novel probabilistic robust design optimization framework is presented here using a Bayesian inference framework. The objective of the proposed study is to obtain probabilistic descriptors of the system parameters conditioned on the user-prescribed target probability distributions of the output qua...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 00457825
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa39264
first_indexed 2018-03-29T13:31:18Z
last_indexed 2018-05-15T12:33:58Z
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spelling 2018-05-15T10:24:24.7317834 v2 39264 2018-03-29 Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2018-03-29 A novel probabilistic robust design optimization framework is presented here using a Bayesian inference framework. The objective of the proposed study is to obtain probabilistic descriptors of the system parameters conditioned on the user-prescribed target probability distributions of the output quantities of interest or figures of merit of a system. A criterion-based identification of a reduced important parameter space is performed from the typically high number of parameters modelling the stochastically parametrized physical system. The criterion can be based on sensitivity indices, design constraints or expert opinion or a combination of these. The posterior probabilities on the reduced or important parameters conditioned on prescribed target distributions of the output quantities of interest is derived using the Bayesian inference framework. The probabilistic optimal design proposed here offers the distinct advantage of prescribing probability bounds of the system performance functions around the optimal design points such that robust operation is ensured. The proposed method has been demonstrated with two numerical examples including the optimal design of a structural dynamic system based on user-prescribed target distribution for the resonance frequency of the system. Journal Article Computer Methods in Applied Mechanics and Engineering 337 281 304 00457825 Bayesian inference; Robust design; Probabilistic optimization; Uncertainty propagation; Stochastic structural dynamics; Sensitivity analysis 31 12 2018 2018-12-31 10.1016/j.cma.2018.03.041 COLLEGE NANME COLLEGE CODE Swansea University 2018-05-15T10:24:24.7317834 2018-03-29T12:41:48.0648040 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised A. Kundu 1 H.G. Matthies 2 M.I. Friswell 3 Michael Friswell 4 0039264-09042018110129.pdf kundu2018(2).pdf 2018-04-09T11:01:29.8100000 Output 4550175 application/pdf Accepted Manuscript true 2019-04-05T00:00:00.0000000 true eng
title Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
spellingShingle Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
Michael Friswell
title_short Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
title_full Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
title_fullStr Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
title_full_unstemmed Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
title_sort Probabilistic optimization of engineering system with prescribed target design in a reduced parameter space
author_id_str_mv 5894777b8f9c6e64bde3568d68078d40
author_id_fullname_str_mv 5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
author Michael Friswell
author2 A. Kundu
H.G. Matthies
M.I. Friswell
Michael Friswell
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 337
container_start_page 281
publishDate 2018
institution Swansea University
issn 00457825
doi_str_mv 10.1016/j.cma.2018.03.041
college_str Faculty of Science and Engineering
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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
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description A novel probabilistic robust design optimization framework is presented here using a Bayesian inference framework. The objective of the proposed study is to obtain probabilistic descriptors of the system parameters conditioned on the user-prescribed target probability distributions of the output quantities of interest or figures of merit of a system. A criterion-based identification of a reduced important parameter space is performed from the typically high number of parameters modelling the stochastically parametrized physical system. The criterion can be based on sensitivity indices, design constraints or expert opinion or a combination of these. The posterior probabilities on the reduced or important parameters conditioned on prescribed target distributions of the output quantities of interest is derived using the Bayesian inference framework. The probabilistic optimal design proposed here offers the distinct advantage of prescribing probability bounds of the system performance functions around the optimal design points such that robust operation is ensured. The proposed method has been demonstrated with two numerical examples including the optimal design of a structural dynamic system based on user-prescribed target distribution for the resonance frequency of the system.
published_date 2018-12-31T07:15:02Z
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score 11.055027

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