Journal Article

Improved data-driven surrogate models by incorporating variable sensitivity for aerodynamic data modeling

Zhuang, Xing, Han, Zhonghua, Guo, Hongjiang, Zhu, Jihong, Xu, Chenzhou, Yang, Yunjie, Jia, Ligan, Kuang, Minchi

2025

Overview

Data-driven surrogate models have become increasingly important in aerospace engineering for the rapid prediction of aerodynamic characteristics. However, when modelling aerodynamic data with varying flight conditions and complex shape parameters, traditional surrogates - such as kriging and fully connected neural network (FCNN) - face major challenges, including high dimensionality, large variable disparities, and limited data availability. Specifically, kriging models suffer from inefficient training processes, while FCNN models struggle with diminished prediction accuracy when confronted with diverse input sets. To address these challenges, this paper introduces two improved surrogate models by incorporating variable sensitivity into the kriging and FCNN models. They employ the analysis of variance to identify the global sensitivity of input variables and utilise K-means clustering to group variables based on their sensitivities. For the kriging model, auxiliary parameters corresponding to the number of clusters are introduced to replace hyperparameters, accelerating model training while maintaining high accuracy. For the FCNN model, input variables are grouped based on their sensitivities, with specialised expert networks handling each group, and a gating network combining their outputs to improve prediction accuracy. The effectiveness of these methods is demonstrated through numerical function examples and two aerodynamic data modelling scenarios: the FDL-5A hypersonic vehicle and the Saenger aerospace plane carrier wing. Results indicate that the proposed approaches significantly enhance the kriging model's training efficiency, achieving a 98% reduction in hyperparameter tuning time compared to conventional method, with minimal sacrifice in accuracy. Simultaneously, the modifications to the FCNN model not only improve its prediction accuracy but also increase its overall practical utility in engineering applications.

• Publisher: Taylor & Francis,Taylor & Francis Ltd,Taylor & Francis Group
• Subject: Accuracy / Aerodynamic characteristics / Aerodynamic data modelling / Aerodynamics / Aerospace engineering / Cluster analysis / Clustering / data-driven surrogate model / Flight conditions / fully connected neural network / Hypersonic vehicles / kriging / Modelling / Neural networks / Parameter sensitivity / Spaceplanes / variable sensitivity / Variables / Vector quantization