Transactions of Nanjing University of Aeronautics & Astronautics
A High-Precision Airfoil Pressure Distribution Prediction Model Based on Hybrid CNN and Chebyshev- Kolmogorov-Arnold Network
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Abstract:
To address the high computational costs of traditional computational fluid dynamics （CFD） in aerodynamic optimization and the limitations of conventional deep learning models， such as slow convergence and insufficient accuracy in capturing complex flow physics， a novel framework integrating convolutional neural network （CNN） and Chebyshev-Kolmogorov-Arnold network （Cheby-KAN） is proposed for 2D airfoil pressure distribution prediction. The framework employs a CNN-based geometry encoder to automatically extract high-dimensional features from signed distance field （SDF） and map them into low-dimensional latent vectors， thereby bypassing traditional manual parameterization. In the regression module， shifted Chebyshev polynomials are introduced as basis functions within the KAN architecture （Cheby-KAN）. Leveraging their orthogonality and superior approximation properties， Cheby-KAN effectively mitigates the Runge phenomenon and enhances numerical stability when resolving high-gradient pressure patterns. Experimental results on a dataset comprising 89 million data points demonstrate that Cheby-KAN achieves a 60% reduction in training time compared to conventional multi-layer perceptron （MLP）， with a 92% decrease in training mean square error （MSE）. Furthermore， the model exhibits exceptional generalization in “zero-shot” tests on unseen airfoils， maintaining a mean absolute error （MAE） of Keywords:
Project Supported:
This work was supported by the the Yangzhou Science and Technology Plan Project (No.YZ2025232).
Clc Number:
V211.3
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