| Citation: | ZHANG Tao, CAI Wenxiang, ZHAO Wei, et al. Prediction of dynamic total pressure distortion index in the distortion generator based on back-propagation artificial neural network[J]. Journal of Aerospace Power, 2025, 40（9）：20230042 doi: 10.13224/j.cnki.jasp.20230042
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The steady state flow field of the total pressure distortion generator was numerically analyzed. The total pressure distortion pattern and steady circumferential distortion index obtained from CFD were in good agreement with the experiment results, which proved the reliability of the mathematical model and method. Then, on the basis of flow field parameters of the inlet/engine aerodynamic interface obtained by numerical analysis, the prediction method of dynamic distortion index was established by using backpropagation artificial neural network based on the improved turbulence correlation model equation, by combining with the turbulence obtained by experiments. After verification, the maximum error of the predicted dynamic total pressure distortion index was 4.19%. The proposed prediction method can be used to guide the related research of the inlet total pressure distortion simulation experiment. Finally, the established turbulence prediction model was used to predict the dynamic distortion pattern of the distortion generator, thus providing an important guidance for its engineering application.
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