Physic-embedded hierarchical Kriging and its application to aerodynamic performance prediction of a tandem cascade

Youwei He; Jieshi Luo; Lingzhi Liu

doi:10.1117/12.3051695

18 November 2024 Physic-embedded hierarchical Kriging and its application to aerodynamic performance prediction of a tandem cascade

Youwei He, Jieshi Luo, Lingzhi Liu

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033M (2024) https://doi.org/10.1117/12.3051695
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Surrogate model plays a vital role in both the engineering design optimization and design space exploration as it can provide fast analytical approximations of the quality of interest. While, current modeling method can only use end-to-end data from the numerical simulation, leaving the valuable distributed physic information of the physic field being unused. To get the full benefit of both the integrated and distributed information, a physic-embedded Hierarchical Kriging modeling method is proposed and utilized to predict the aerodynamic performance of a tandem cascade for axial flow compressor. This modeling method extracts low-dimensional physical features from the pressure distribution of the cascade surface and then improves the modeling accuracy of the static pressure of the cascade effectively via feature embedding. Compared with traditional modeling approach, the proposed method can improve the modeling accuracy more than 20%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Youwei He, Jieshi Luo, and Lingzhi Liu "Physic-embedded hierarchical Kriging and its application to aerodynamic performance prediction of a tandem cascade", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033M (18 November 2024); https://doi.org/10.1117/12.3051695

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