Bayesian model updating and class selection of a wing-engine structure with nonlinear connections using nonlinear normal modes

Song, Mingming; Renson, Ludovic; Moaveni, Babak; Kerschen, Gaëtan

doi:10.1016/j.ymssp.2021.108337

Cited by 24 publications

(6 citation statements)

References 63 publications

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“…Song et.al. utilised TMCMC in successfully selecting a nonlinear model for a wing engine structure [32].…”

Section: Introductionmentioning

confidence: 99%

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

McGurk,

Lye,

Renson

et al. 2024

AIAA Journal

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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.

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“…Song et.al. utilised TMCMC in successfully selecting a nonlinear model for a wing engine structure [32].…”

Section: Introductionmentioning

confidence: 99%

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

McGurk,

Lye,

Renson

et al. 2024

AIAA Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Weights have been applied to the measured data [1] and various regularization methods have been used to improve the conditioning of the inverse problem [3,4]. Recently, Bayesian methods for model updating [5] have been heavily researched, and non-linear models were also updated [6,7]. Because the cost function is usually smooth and convex near the optimal solution [8], the Interior Point Method (IPM) [9,10] can be used to solve the minimization problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-level curvature-based parametrization and model updating using a 3D full-field response

Zaletelj

Gorjup²,

Slavič³

et al. 2023

Mechanical Systems and Signal Processing

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“…Song et.al. utilized TMCMC in successfully selecting a nonlinear model for a wing engine structure [30].…”

Section: Introductionmentioning

confidence: 99%

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

McGurk,

Lye,

Renson

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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 modelled 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 20 % improvement in accuracy compared to conventional deterministic methods. Transitional Markov Chain Monte-Carlo was identified as the optimal choice of the sampling method for stochastic model identification. In selecting alternative nonlinear models, multi-modal solutions were identified that provided a closer representation of physical behaviour suggesting the mathematical model requires further modification.

show abstract

Bayesian model updating and class selection of a wing-engine structure with nonlinear connections using nonlinear normal modes

Cited by 24 publications

References 63 publications

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

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

Multi-level curvature-based parametrization and model updating using a 3D full-field response

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

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