Multi-fidelity nonlinear unsteady aerodynamic modeling and uncertainty estimation based on Hierarchical Kriging

Peng, Xuhao; Kou, Jiaqing; Zhang, Weiwei

doi:10.1016/j.apm.2023.05.031

Cited by 13 publications

(2 citation statements)

References 48 publications

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“…Kriging models have been found to be remarkably effective in modeling complex nonlinear systems with the added benefit of requiring only minimal training data [54]. A major benefit of Kriging is the ability to seamlessly integrate new observations into the current model framework [55,56]. Kriging surrogate models have been employed to propagate uncertainty in bifurcation diagrams of landing gear designs by Tartaruga [57] and ring damper designs by Sun et.al.…”

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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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 fusion of multiple information sources into a single surrogate model has readily been applied to a very wide range of industrial design problems, from ship component [28,41,43] and civil infrastructure [13,40] design to traffic state estimation [1] and inter-satellite calibration [10]. Aeroplane component design in particular often relies on multi-fidelity surrogate models [6,26,32,37,50,52], where https://doi.org/10.1017/S1446181124000087 Published online by Cambridge University Press [3] Mf-EBB modelling: methodology and challenges 3 expensive wind tunnel data might be supplemented with much more abundant but less reliable computational fluid dynamics (CFD) data. When using surrogate modelling techniques in Mf-EBB problems, many high-level decisions need to be made.…”

Section: Introductionmentioning

confidence: 99%

Methodology and Challenges of Surrogate Modelling Methods for Multi-Fidelity Expensive Black-Box Problems

ANDRÉS-THIÓ,

MUÑOZ,

SMITH-MILES

2024

ANZIAM J.

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Many industrial design problems are characterized by a lack of an analytical expression defining the relationship between design variables and chosen quality metrics. Evaluating the quality of new designs is therefore restricted to running a predetermined process such as physical testing of prototypes. When these processes carry a high cost, choosing how to gather further data can be very challenging, whether the end goal is to accurately predict the quality of future designs or to find an optimal design. In the multi-fidelity setting, one or more approximations of a design’s performance are available at varying costs and accuracies. Surrogate modelling methods have long been applied to problems of this type, combining data from multiple sources into a model which guides further sampling. Many challenges still exist; however, the foremost among them is choosing when and how to rely on available low-fidelity sources. This tutorial-style paper presents an introduction to the field of surrogate modelling for multi-fidelity expensive black-box problems, including classical approaches and open questions in the field. An illustrative example using Australian elevation data is provided to show the potential downfalls in blindly trusting or ignoring low-fidelity sources, a question that has recently gained much interest in the community.

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Theta-regularized Kriging: Modeling and algorithms

Xie,

2024

Applied Mathematical Modelling

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Multi-fidelity nonlinear unsteady aerodynamic modeling and uncertainty estimation based on Hierarchical Kriging

Cited by 13 publications

References 48 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

Methodology and Challenges of Surrogate Modelling Methods for Multi-Fidelity Expensive Black-Box Problems

Theta-regularized Kriging: Modeling and algorithms

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