Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head

A Gaussian process regression (GPR) model based on an improved automatic kernel construction (AKC) algorithm using beam search is proposed to establish a surrogate model between lift body shape parameters and aerodynamic coefficients with various training sets sizes. The precision of our proposed surrogate model is assessed through tenfold cross-validation. The improved AKC-GPR algorithm, polynomial regression, and support vector regression (SVR) are employed to construct the regression model. The interpolation and extrapolation capabilities of the model, as generated by the improved AKC-GPR algorithm, are examined using six shapes beyond the sample set. The results show that the three models perform similarly with a large training set. However, when the training set size is less than 40% sample dataset, the model constructed by the improved AKC-GPR algorithm has better fitting and prediction capabilities than the other models. Specifically, the max relative error of the improved model is one-fourth of that of SVR and one-half of that of polynomial regression with the training set size of 8% of the sample dataset. Furthermore, the lift-to-drag ratio relative error of interpolation is only 3%, and extrapolation error is 6%. In terms of the fitting and prediction abilities for small samples, the lift-to-drag ratio model outperforms the drag coefficient model, while the lift coefficient model performs the poorest. These findings suggest that the proposed AKC-GPR algorithm can be an effective approach for building a surrogate model in the field of aerodynamics.

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Machine learning-accelerated aerodynamic inverse design

Shirvani

Nili-Ahmadabadi

2023

Engineering Applications of Computational Fluid Mechanics

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Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head

Cited by 5 publications

References 21 publications

Aerodynamic shape optimization based on proper orthogonal decomposition reparameterization under small training sets

Aerodynamic shape optimization based on proper orthogonal decomposition reparameterization under small training sets

Improved automatic kernel construction for Gaussian process regression in small sample learning for predicting lift body aerodynamic performance

Machine learning-accelerated aerodynamic inverse design

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