Data-driven Bayesian inference for stochastic model identification of nonlinear aeroelastic systems

Michael McGurk, Adolphus Lye, Ludovic Renson, Jie Yuan

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The objective of this work is to propose a data-driven Bayesian inference framework to efficiently identify parameters and select models of nonlinear aeroelastic systems. The framework consists of the use of Bayesian theory together with advanced kriging surrogate models to effectively represent the limit cycle oscillation response of nonlinear aeroelastic systems. Three types of sampling methods, namely, Markov chain Monte Carlo, transitional Markov chain Monte Carlo, and the sequential Monte Carlo sampler, are implemented into Bayesian model updating. The framework has been demonstrated using a nonlinear wing flutter test rig. It is modeled by a two-degree-of-freedom aeroelastic system and solved by the harmonic balance methods. The experimental data of the flutter wing is obtained using control-based continuation techniques. The proposed methodology provided up to a 20% improvement in accuracy compared to conventional deterministic methods and significantly increased computational efficiency in the updating and uncertainty quantification processes. Transitional Markov chain Monte Carlo was identified as the optimal choice of sampling method for stochastic model identification. In selecting alternative nonlinear models, multimodal solutions were identified that provided a closer representation of the physical behavior of the complex aeroelastic system than a single solution.
Original languageEnglish
Pages (from-to)1889-1905
Number of pages17
JournalAIAA Journal
Volume62
Issue number5
DOIs
Publication statusPublished - May 2024

Keywords

  • Nonlinear Aeroelastic Systems
  • Uncertainty Quantification
  • Structural Dynamics and Characterization
  • Applied Mathematics
  • Aerospace Engineering
  • Surrogate Model
  • Bayesian model updating
  • Nonlinear Aeroelasticity
  • Limit cycle oscillation

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