A multi-fidelity surrogate modeling method based on variance-weighted sum for the fusion of multiple non-hierarchical low-fidelity data

Cheng, Meng; Jiang, Ping; Hu, Jiexiang; Shu, Leshi; Zhou, Qi

doi:10.1007/s00158-021-03055-2

Cited by 13 publications

(2 citation statements)

References 46 publications

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“…Co-Kriging, 2000 [22,24,30] √ Bayesian HK, 2012 [25] √ Bayesian MCK, 2012 [24] √ Bayesian IHK, 2018 [12] √ Bayesian MVCM, 2015 [19] √ BF GVFM, 2015 [20] √ BF co-BRF, 2017 [40] √ Bayesian MFGP, 2018 [41] √ Bayesian POD-co-Kriging, 2018 [42] √ Bayesian SM-VFM, 2018 [26] √ SM, BF MHK, 2020 [31,32] √ Bayesian MDNN, 2020 [13] √ BF GCK, 2020 [43,44] √ Bayesian TL-VFSM, 2021 [27] √ BF GAN-MDF, 2022 [29] √ BF MMGP, 2021 [45] √ Bayesian WS, PC-DIT, PC-CSC, 2016 [39] √ Bayesian ECK, 2018 [36] √ Bayesian LRMFS, 2018 [37] √ BF VWS-MFS, 2021 [46] √ Bayesian NHLF-co-Kriging, 2022 [38] √ Bayesian Our work (EHK)…”

Section: Granularity Of Lf Datasets Mfsm Framework Type Multi-level N...mentioning

confidence: 99%

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Pham,

Tyan,

Nguyen

et al. 2023

Aerospace

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Multi-fidelity surrogate modeling (MFSM) methods are gaining recognition for their effectiveness in addressing simulation-based design challenges. Prior approaches have typically relied on recursive techniques, combining a limited number of high-fidelity (HF) samples with multiple low-fidelity (LF) datasets structured in hierarchical levels to generate a precise HF approximation model. However, challenges arise when dealing with non-level LF datasets, where the fidelity levels of LF models are indistinguishable across the design space. In such cases, conventional methods employing recursive frameworks may lead to inefficient LF dataset utilization and substantial computational costs. To address these challenges, this work proposes the extended hierarchical Kriging (EHK) method, designed to simultaneously incorporate multiple non-level LF datasets for improved HF model construction, regardless of minor differences in fidelity levels. This method leverages a unique Bayesian-based MFSM framework, simultaneously combining non-level LF models using scaling factors to construct a global trend model. During model processing, unknown scaling factors are implicitly estimated through hyperparameter optimization, resulting in minimal computational costs during model processing, regardless of the number of LF datasets integrated, while maintaining the necessary accuracy in the resulting HF model. The advantages of the proposed EHK method are validated against state-of-the-art MFSM methods through various analytical examples and an engineering case study involving the construction of an aerodynamic database for the KP-2 eVTOL aircraft under various flying conditions. The results demonstrated the superiority of the proposed method in terms of computational cost and accuracy when generating aerodynamic models from the given multi-fidelity datasets.

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Section: Granularity Of Lf Datasets Mfsm Framework Type Multi-level N...mentioning

confidence: 99%

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Pham,

Tyan,

Nguyen

et al. 2023

Aerospace

View full text Add to dashboard Cite

show abstract

“…Before optimization of the surrogate model, the test set was used to verify the performance of the constructed GA-GRNN surrogate model. The maximum absolute error (MAE), root mean square error (RMSE), and coefficient of determination R 2 were selected as the indicators to evaluate the performance [28,29]. MAE evaluates the local prediction accuracy of the GA-GRNN model, RMSE evaluates the global prediction accuracy of the GA-GRNN surrogate model, and R 2 evaluates the fit of the GA-GRNN surrogate model.…”

Section: Ga-grnn Surrogate Modelmentioning

confidence: 99%

Aerodynamic Shape Optimization of a Square Cylinder with Multi-Parameter Corner Recession Modifications

et al. 2022

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Corner modifications can reduce wind loads acting on supertall buildings and modify the corresponding flow structures. The present study investigated the aerodynamic shape optimization of the corner recession square cylinders with multiple geometric parameters in a large design space via the GA-GRNN surrogate model updating-based multi-objective optimization framework. Six typical optimal aerodynamic shape sections M1~M6 were selected from the Pareto optimal front, and the effects of multiple geometric parameters of these sections on the aerodynamic performance and flow field were analyzed. The results showed that the present multi-objective optimization framework can significantly reduce the computational load and time cost, and significantly improve the optimization efficiency in solving complex engineering problems. The optimal corner recession sections can obviously reduce the mean drag coefficient CD and root mean square lift coefficient CσL while significantly increasing the Strouhal number St of the square cylinder, and it is concluded that the aerodynamic shape optimization can significantly improve the aerodynamic performance of square-sectional supertall buildings. When compared with the benchmark section, the CD and CσL of the optimal section M1 can be reduced up to 45.7% and 84.5%, respectively. Based on the analysis of the flow structures around the optimal sections, the flow mechanism can be attributed to the fact that the corner recession modifications postpone the flow separation, and deflect the separated shear layer towards the side surfaces and suppress the development of vortex shedding in the wake, which leads to significant elongation of the wake length and reduction of the width of the recirculation region. The proposed multi-objective optimization framework in this study can provide an important reference for the aerodynamic shape optimization of building structures and relevant studies.

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A novel multi-fidelity surrogate modeling framework integrated with sequential sampling criterion for non-hierarchical data

Xiong,

Huang,

Xie

et al. 2024

Struct Multidisc Optim

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A multi-fidelity surrogate modeling method based on variance-weighted sum for the fusion of multiple non-hierarchical low-fidelity data

Cited by 13 publications

References 46 publications

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Aerodynamic Shape Optimization of a Square Cylinder with Multi-Parameter Corner Recession Modifications

A novel multi-fidelity surrogate modeling framework integrated with sequential sampling criterion for non-hierarchical data

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