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A sequential optimization framework for simultaneous design variables optimization and probability uncertainty allocation

A sequential optimization framework for simultaneous design variables optimization and probability uncertainty allocation
A sequential optimization framework for simultaneous design variables optimization and probability uncertainty allocation
In engineering design, the performance of the system and the budget of design uncertainty should be balanced, which means that it is best to optimize design variables and allocate the manufacturing uncertainty simultaneously. This work formulates this problem as an uncertainty optimization problem, where the input uncertainty is modeled by the probability method and both the design variables and the uncertainty magnitude are included in the optimization variables. A sequence optimization framework is proposed to solve the optimization problem. The Taylor-based first-order method is used to translate the probability constraint into a deterministic constraint. A correction coefficient is calculated by the dimensional adaptive polynomial chaos expansion method to improve the accuracy of the uncertainty analysis. The constraint translation and the correction coefficient calculation are executed sequentially. The accuracy and effectiveness of the proposed framework are validated by three benchmark problems, including a mathematical problem, a cantilever I-beam, and a ten-bar truss case.
Dimensional adaptive sparse grid, Polynomial chaos expansion, Sequence optimization framework, Taylor-based uncertainty analysis, Uncertainty allocation
1615-147X
Fang, H
aeadc5a7-f442-4108-8ea0-995bcc8a5383
Gong, C
6512ba76-57c0-4843-8a6e-1e5716460ad8
Li, C.
68f80653-ca21-4611-a2c9-ee216e14fc67
Zhang, Y
95eb05e7-f0f3-494f-8f1b-fa27885d7f9a
Da Ronch, A.
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Fang, H
aeadc5a7-f442-4108-8ea0-995bcc8a5383
Gong, C
6512ba76-57c0-4843-8a6e-1e5716460ad8
Li, C.
68f80653-ca21-4611-a2c9-ee216e14fc67
Zhang, Y
95eb05e7-f0f3-494f-8f1b-fa27885d7f9a
Da Ronch, A.
a2f36b97-b881-44e9-8a78-dd76fdf82f1a

Fang, H, Gong, C, Li, C., Zhang, Y and Da Ronch, A. (2020) A sequential optimization framework for simultaneous design variables optimization and probability uncertainty allocation. Structural and Multidisciplinary Optimization. (doi:10.1007/s00158-020-02759-1).

Record type: Article

Abstract

In engineering design, the performance of the system and the budget of design uncertainty should be balanced, which means that it is best to optimize design variables and allocate the manufacturing uncertainty simultaneously. This work formulates this problem as an uncertainty optimization problem, where the input uncertainty is modeled by the probability method and both the design variables and the uncertainty magnitude are included in the optimization variables. A sequence optimization framework is proposed to solve the optimization problem. The Taylor-based first-order method is used to translate the probability constraint into a deterministic constraint. A correction coefficient is calculated by the dimensional adaptive polynomial chaos expansion method to improve the accuracy of the uncertainty analysis. The constraint translation and the correction coefficient calculation are executed sequentially. The accuracy and effectiveness of the proposed framework are validated by three benchmark problems, including a mathematical problem, a cantilever I-beam, and a ten-bar truss case.

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A Framework_ver.3 - Accepted Manuscript
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Accepted/In Press date: 5 October 2020
e-pub ahead of print date: 31 October 2020
Keywords: Dimensional adaptive sparse grid, Polynomial chaos expansion, Sequence optimization framework, Taylor-based uncertainty analysis, Uncertainty allocation

Identifiers

Local EPrints ID: 447774
URI: http://eprints.soton.ac.uk/id/eprint/447774
ISSN: 1615-147X
PURE UUID: 20b1db15-850f-4d12-a6a7-4dbc065a41d8
ORCID for A. Da Ronch: ORCID iD orcid.org/0000-0001-7428-6935

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Date deposited: 19 Mar 2021 17:37
Last modified: 28 Apr 2022 05:34

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Contributors

Author: H Fang
Author: C Gong
Author: C. Li
Author: Y Zhang
Author: A. Da Ronch ORCID iD

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