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Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures / Tanmoy Chatterjee, Danilo Karlicic, Sondipon Adhikari, Michael Friswell

Mechanical Systems and Signal Processing, Volume: 149, Start page: 107218

Swansea University Authors: Tanmoy Chatterjee, Danilo Karlicic, Sondipon Adhikari, Michael Friswell

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Abstract

This paper characterizes the stochastic dynamic response of periodic structures by accounting for manufacturing variabilities. Manufacturing variabilities are simulated through a probabilistic description of the structural material and geometric properties. The underlying uncertainty propagation pro...

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Published in: Mechanical Systems and Signal Processing
ISSN: 0888-3270
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa55081
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spelling 2020-10-22T15:33:08.5668672 v2 55081 2020-08-27 Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures 5e637da3a34c6e97e2b744c2120db04d Tanmoy Chatterjee Tanmoy Chatterjee true false d99ee591771c238aab350833247c8eb9 0000-0002-7547-9293 Danilo Karlicic Danilo Karlicic true false 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2020-08-27 AERO This paper characterizes the stochastic dynamic response of periodic structures by accounting for manufacturing variabilities. Manufacturing variabilities are simulated through a probabilistic description of the structural material and geometric properties. The underlying uncertainty propagation problem has been efficiently carried out by functional decomposition in the stochastic space with the help of Gaussian Process (GP) meta-modelling. The decomposition is performed by projected the response onto the eigenspace and involves a nominal number of actual physics-based function evaluations (the eigenvalue analysis). This allows the stochastic dynamic response evaluation to be solved with low computational cost. Two numerical examples, namely an analytical model of a damped mechanical chain and a finite-element model of multiple beam-mass systems, are undertaken. Two key findings from the results are that the proposed GP based approximation scheme is capable of (i) capturing the stochastic dynamic response in systems with well-separated modes in the presence of high levels of uncertainties (up to 20), and (ii) adequately capturing the stochastic dynamic response in systems with multiple sets of identical modes in the presence of 5–10 uncertainty. The results are validated by Monte Carlo simulations. Journal Article Mechanical Systems and Signal Processing 149 107218 Elsevier BV 0888-3270 Mechanical chain, Multiple beam-mass system, Mode degeneration, Eigensolution, Gaussian process 15 2 2021 2021-02-15 10.1016/j.ymssp.2020.107218 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2020-10-22T15:33:08.5668672 2020-08-27T11:02:35.8965072 College of Engineering Engineering Tanmoy Chatterjee 1 Danilo Karlicic 0000-0002-7547-9293 2 Sondipon Adhikari 3 Michael Friswell 4 55081__18076__288490d6cebd415a8f8eb31734163648.pdf 55081.pdf 2020-08-27T17:08:46.1520858 Output 1006909 application/pdf Accepted Manuscript true 2021-08-24T00:00:00.0000000 © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ true English
title Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
spellingShingle Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
Tanmoy, Chatterjee
Danilo, Karlicic
Sondipon, Adhikari
Michael, Friswell
title_short Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
title_full Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
title_fullStr Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
title_full_unstemmed Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
title_sort Gaussian process assisted stochastic dynamic analysis with applications to near-periodic structures
author_id_str_mv 5e637da3a34c6e97e2b744c2120db04d
d99ee591771c238aab350833247c8eb9
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5894777b8f9c6e64bde3568d68078d40
author_id_fullname_str_mv 5e637da3a34c6e97e2b744c2120db04d_***_Tanmoy, Chatterjee
d99ee591771c238aab350833247c8eb9_***_Danilo, Karlicic
4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon, Adhikari
5894777b8f9c6e64bde3568d68078d40_***_Michael, Friswell
author Tanmoy, Chatterjee
Danilo, Karlicic
Sondipon, Adhikari
Michael, Friswell
author2 Tanmoy Chatterjee
Danilo Karlicic
Sondipon Adhikari
Michael Friswell
format Journal article
container_title Mechanical Systems and Signal Processing
container_volume 149
container_start_page 107218
publishDate 2021
institution Swansea University
issn 0888-3270
doi_str_mv 10.1016/j.ymssp.2020.107218
publisher Elsevier BV
college_str College of Engineering
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hierarchy_parent_title College of Engineering
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description This paper characterizes the stochastic dynamic response of periodic structures by accounting for manufacturing variabilities. Manufacturing variabilities are simulated through a probabilistic description of the structural material and geometric properties. The underlying uncertainty propagation problem has been efficiently carried out by functional decomposition in the stochastic space with the help of Gaussian Process (GP) meta-modelling. The decomposition is performed by projected the response onto the eigenspace and involves a nominal number of actual physics-based function evaluations (the eigenvalue analysis). This allows the stochastic dynamic response evaluation to be solved with low computational cost. Two numerical examples, namely an analytical model of a damped mechanical chain and a finite-element model of multiple beam-mass systems, are undertaken. Two key findings from the results are that the proposed GP based approximation scheme is capable of (i) capturing the stochastic dynamic response in systems with well-separated modes in the presence of high levels of uncertainties (up to 20), and (ii) adequately capturing the stochastic dynamic response in systems with multiple sets of identical modes in the presence of 5–10 uncertainty. The results are validated by Monte Carlo simulations.
published_date 2021-02-15T04:13:55Z
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