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A multi-level data-driven Bayesian approach for stochastic model updating of complex aeroelastic systems

A multi-level data-driven Bayesian approach for stochastic model updating of complex aeroelastic systems
A multi-level data-driven Bayesian approach for stochastic model updating of complex aeroelastic systems

The objective of this work is to propose a multilevel data-driven Bayesian framework to minimise the sampling requirement for stochastic model identification of general nonlinear aeroelastic systems. Adaptive Kriging based surrogate models are developed through multi-level Bayesian modeling updating to represent limit cycle oscillation (LCO) response of the nonlinear aeroelastic model. The proposed methodology is demonstrated on an aerofoil wing flutter model together with LCO experimental testing data. The results is benchmarked by the counterpart from a classical single-level approach. It is observed that the proposed approach offered a 74% reduction in training data requirement/run time and 3% accuracy improvement for the surrogate model.

Aerospace Research Central
McGurk, Michael
ff8abe6b-24b8-4d53-8af2-c735ddf26d4f
Yuan, Jie
4bcf9ce8-3af4-4009-9cd0-067521894797
McGurk, Michael
ff8abe6b-24b8-4d53-8af2-c735ddf26d4f
Yuan, Jie
4bcf9ce8-3af4-4009-9cd0-067521894797

McGurk, Michael and Yuan, Jie (2024) A multi-level data-driven Bayesian approach for stochastic model updating of complex aeroelastic systems. In AIAA SCITECH 2024 Forum. Aerospace Research Central. 13 pp . (doi:10.2514/6.2024-0193).

Record type: Conference or Workshop Item (Paper)

Abstract

The objective of this work is to propose a multilevel data-driven Bayesian framework to minimise the sampling requirement for stochastic model identification of general nonlinear aeroelastic systems. Adaptive Kriging based surrogate models are developed through multi-level Bayesian modeling updating to represent limit cycle oscillation (LCO) response of the nonlinear aeroelastic model. The proposed methodology is demonstrated on an aerofoil wing flutter model together with LCO experimental testing data. The results is benchmarked by the counterpart from a classical single-level approach. It is observed that the proposed approach offered a 74% reduction in training data requirement/run time and 3% accuracy improvement for the surrogate model.

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Scitech_final_paper (7) - Accepted Manuscript
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Published date: 4 January 2024
Additional Information: Publisher Copyright: © 2024 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Venue - Dates: 2024 AIAA SciTech Forum, Hyatt Regency Orlando, Orlando, United States, 2024-01-08 - 2024-01-12

Identifiers

Local EPrints ID: 486475
URI: http://eprints.soton.ac.uk/id/eprint/486475
PURE UUID: 3dac902f-ca28-47a4-96e4-f69d1fc69c5b
ORCID for Jie Yuan: ORCID iD orcid.org/0000-0002-2411-8789

Catalogue record

Date deposited: 24 Jan 2024 17:33
Last modified: 20 Jun 2024 02:04

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Contributors

Author: Michael McGurk
Author: Jie Yuan ORCID iD

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