| Citation: | LIU Yupeng, LIU Yong, LI Yunzhu, et al. Flutter prediction based on various deep learning models for compressor swept-curved blade[J]. Journal of Aerospace Power, 2025, 40（4）：20240538 doi: 10.13224/j.cnki.jasp.20240538
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In view of the problem of blade flutter caused by unsteady fluid-solid coupling in compressor, a multi-physical field prediction and flutter characteristics identification method based on deep learning was proposed. A set of end-to-end prediction methods from the design variables of compressor blade to the three-dimensional physical field parameters of blade surface and then to flutter characteristics were constructed by using various deep learning operators. Four deep learning models, UNet, FNO, Transformer and GMM, were compared respectively. Among them, the Transformer model combined with multi-head linear self-attention mechanism and Fourier layer had higher prediction accuracy in the prediction of physical field distribution and aerodynamic damping coefficient recognition tasks of three-dimensional blade surface. For Transformer model, the average relative deviation of the physical field prediction was about 0.005, and the maximum relative deviation was about 0.05. The relative deviation of the minimum prediction of the flutter parameters and the aerodynamic damping was within ±7.5%, of which more than 50% of the relative deviation fell within ±2.5%. The average absolute value of the relative deviation was less than 3%, and the prediction of 32 examples can be completed within 7 milliseconds.
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