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Bayesian calibration for large-scale fluid structure interaction problems under embedded/immersed boundary framework

Cao, Shunxiang and Huang, Daniel Zhengyu (2022) Bayesian calibration for large-scale fluid structure interaction problems under embedded/immersed boundary framework. International Journal for Numerical Methods in Engineering, 123 (8). pp. 1791-1812. ISSN 0029-5981. doi:10.1002/nme.6916. https://resolver.caltech.edu/CaltechAUTHORS:20220106-42439900

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Abstract

Bayesian calibration is widely used for inverse analysis and uncertainty analysis for complex systems in the presence of both computer models and observation data. In the present work, we focus on large-scale fluid-structure interaction systems characterized by large structural deformations. Numerical methods to solve these problems, including embedded/immersed boundary methods, are typically not differentiable and lack smoothness. We propose a framework that is built on unscented Kalman filter/inversion to efficiently calibrate and provide uncertainty estimations of such complicated models with noisy observation data. The approach is derivative-free and non-intrusive, and is of particular value for the forward model that is computationally expensive and provided as a black box which is impractical to differentiate. The framework is demonstrated and validated by successfully calibrating the model parameters of a piston problem and identifying the damage field of an aircraft wing under transonic buffeting.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1002/nme.6916DOIArticle
https://github.com/Zhengyu-Huang/InverseProblems.jlRelated ItemCode
Additional Information:© 2022 John Wiley & Sons Ltd. Issue Online: 22 March 2022; Version of Record online: 16 February 2022; Accepted manuscript online: 04 January 2022; Manuscript accepted: 28 December 2021; Manuscript received: 15 December 2021. The authors gratefully acknowledge the support of National Institutes of Health under Award P01-DK043881 and the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. The authors thank Dr. Kevin G. Wang and Dr. Fangbao Tian for their advice on the manuscript. Data Availability Statement: All computer code used in this article is open source. Datasets, including mesh files and results, are available at https://github.com/Zhengyu-Huang/InverseProblems.jl.
Funders:
Funding AgencyGrant Number
NIHP01-DK043881
Schmidt Futures ProgramUNSPECIFIED
Subject Keywords:Bayesian calibration; derivative-free optimization; embedded/immersed boundary method; fluid-structure interaction; uncertainty quantification
Issue or Number:8
DOI:10.1002/nme.6916
Record Number:CaltechAUTHORS:20220106-42439900
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220106-42439900
Official Citation:Cao, S, Zhengyu Huang, D. Bayesian calibration for large-scale fluid structure interaction problems under embedded/immersed boundary framework. Int J Numer Methods Eng. 2022; 123(8): 1791-1812. doi:10.1002/nme.6916
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112746
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Jan 2022 22:21
Last Modified:13 Apr 2022 17:43

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