Evaluation of global sensitivity analysis methods for computational structural mechanics problems
Evaluation of global sensitivity analysis methods for computational structural mechanics problems
The curse of dimensionality confounds the comprehensive evaluation of computational structural mechanics problems. Adequately capturing complex material behavior and interacting physics phenomenon in models can lead to long run times and memory requirements resulting in the need for substantial computational resources to analyze one scenario for a single set of input parameters. The computational requirements are then compounded when considering the number and range of input parameters spanning material properties, loading, boundary conditions, and model geometry that must be evaluated to characterize behavior, identify dominant parameters, perform uncertainty quantification, and optimize performance. To reduce model dimensionality, global sensitivity analysis (GSA) enables the identification of dominant input parameters for a specific structural performance output. However, many distinct types of GSA methods are available, presenting a challenge when selecting the optimal approach for a specific problem. While substantial documentation is available in the literature providing details on the methodology and derivation of GSA methods, application-based case studies focus on fields such as finance, chemistry, and environmental science. To inform the selection and implementation of a GSA method for structural mechanics problems for a nonexpert user, this article investigates five of the most widespread GSA methods with commonly used structural mechanics methods and models of varying dimensionality and complexity. It is concluded that all methods can identify the most dominant parameters, although with significantly different computational costs and quantitative capabilities. Therefore, method selection is dependent on computational resources, information required from the GSA, and available data.
computational modeling, finite element analysis, peridynamics, sensitivity analysis, surrogate modeling
Crusenberry, Cody R.
e6464df1-5747-4494-959c-6fa81ec6fb9b
Sobey, Adam J.
e850606f-aa79-4c99-8682-2cfffda3cd28
TerMaath, Stephanie C.
6ec5875a-5086-40cc-a910-e5e453653f52
9 November 2023
Crusenberry, Cody R.
e6464df1-5747-4494-959c-6fa81ec6fb9b
Sobey, Adam J.
e850606f-aa79-4c99-8682-2cfffda3cd28
TerMaath, Stephanie C.
6ec5875a-5086-40cc-a910-e5e453653f52
Crusenberry, Cody R., Sobey, Adam J. and TerMaath, Stephanie C.
(2023)
Evaluation of global sensitivity analysis methods for computational structural mechanics problems.
Data-Centric Engineering, 4 (5), [e28].
(doi:10.1017/dce.2023.23).
Abstract
The curse of dimensionality confounds the comprehensive evaluation of computational structural mechanics problems. Adequately capturing complex material behavior and interacting physics phenomenon in models can lead to long run times and memory requirements resulting in the need for substantial computational resources to analyze one scenario for a single set of input parameters. The computational requirements are then compounded when considering the number and range of input parameters spanning material properties, loading, boundary conditions, and model geometry that must be evaluated to characterize behavior, identify dominant parameters, perform uncertainty quantification, and optimize performance. To reduce model dimensionality, global sensitivity analysis (GSA) enables the identification of dominant input parameters for a specific structural performance output. However, many distinct types of GSA methods are available, presenting a challenge when selecting the optimal approach for a specific problem. While substantial documentation is available in the literature providing details on the methodology and derivation of GSA methods, application-based case studies focus on fields such as finance, chemistry, and environmental science. To inform the selection and implementation of a GSA method for structural mechanics problems for a nonexpert user, this article investigates five of the most widespread GSA methods with commonly used structural mechanics methods and models of varying dimensionality and complexity. It is concluded that all methods can identify the most dominant parameters, although with significantly different computational costs and quantitative capabilities. Therefore, method selection is dependent on computational resources, information required from the GSA, and available data.
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More information
Accepted/In Press date: 30 September 2023
e-pub ahead of print date: 9 November 2023
Published date: 9 November 2023
Additional Information:
Funding Information:
This research was supported by the United States Office of Naval Research (ONR) grant N00014-21-1-2041 under the direction of Dr. Paul Hess. It was also supported by the Lloyd’s Register Foundation.
Funding Information:
This research was supported by the United States Office of Naval Research (ONR) grant N00014-21-1-2041 under the direction of Dr. Paul Hess. It was also supported by the Lloyd’s Register Foundation.
Publisher Copyright:
© The Author(s), 2023. Published by Cambridge University Press.
Keywords:
computational modeling, finite element analysis, peridynamics, sensitivity analysis, surrogate modeling
Identifiers
Local EPrints ID: 484737
URI: http://eprints.soton.ac.uk/id/eprint/484737
ISSN: 2632-6736
PURE UUID: 3728c22a-1f6b-467d-a798-d3ced34a3368
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Date deposited: 20 Nov 2023 17:58
Last modified: 06 Jun 2024 01:45
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Author:
Cody R. Crusenberry
Author:
Stephanie C. TerMaath
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