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 EMD, 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 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.

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