Data-Driven Bayesian Inference for Stochastic Model Identification of Nonlinear Aeroelastic Systems

被引:4
|
作者
McGurk, Michael [1 ]
Lye, Adolphus [2 ]
Renson, Ludovic [3 ]
Yuan, Jie [4 ]
机构
[1] Univ Strathclyde, Aerosp Ctr Excellence, Glasgow G1 1XJ, Scotland
[2] Natl Univ Singapore, Singapore Nucl Res & Safety Initiat, Singapore 138602, Singapore
[3] Imperial Coll London, Dynam Grp, South Kensington Campus, London SW7 2AZ, England
[4] Univ Southampton, Comp Engn Design Grp, Southampton SO17 1BF, England
基金
英国工程与自然科学研究理事会;
关键词
Nonlinear Aeroelastic Systems; Uncertainty Quantification; Structural Dynamics and Characterization; Applied Mathematics; Aerospace Engineering; Surrogate Model; Bayesian model updating; Nonlinear Aeroelasticity; Limit cycle oscillation; LIMIT-CYCLE OSCILLATIONS; HARMONIC-BALANCE METHOD; BIFURCATION-ANALYSIS; UNCERTAINTY QUANTIFICATION; STABILITY; DESIGN;
D O I
10.2514/1.J063611
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
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.
引用
收藏
页码:1889 / 1905
页数:17
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