An Interpretable Aerodynamic Identification Model for Hypersonic Wind Tunnels

Abstract

Aerodynamic identification accuracy is one of the key factors determining the success or failure of hypersonic aircraft development. However, the inertial force (mid-frequency) generated by the shock flow and the instrument noise (high-frequency) introduced by the acquisition equipment seriously affect the identification accuracy. To address this challenge, first, a convolutional neural network is introduced to filter out high-frequency noise, and the influence of kernel size on feature extraction ability is discussed. Second, a dense block with adaptive empirical mode decomposition, which filters out inertial force component, alleviates the dependence of the model on the number of samples, and gives a distinct physical meaning to the output of each layer, is proposed. Based on the above-mentioned research, an aerodynamic identification model based on a large convolutional kernel and dense block (AI-LSK&DB) is proposed. Wind tunnel experimental results show that the identification accuracy, robustness, and stability of AI-LSK&DB are significantly improved compared with those of frequency domain models and deep learning models.