| Citation: | QU Jingguo, WANG Qiang, PENG Bo, et al. Icing prediction method for arbitrary symmetric airfoil using multimodal fusion[J]. Journal of Aerospace Power, 2024, 39（1）：20220143 doi: 10.13224/j.cnki.jasp.20220143
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A deep neural network method based on multimodal fusion was adopted to solve the problem that most current neural network ice prediction methods can only target specific airfoils and do not have the universality of multi-airfoil features. This method used the airfoil cross-section image and the icing condition parameters as inputs, and the two-dimensional ice curve Fourier series fitting parameters as outputs. This deep neural network prediction model realized the prediction ability of the ice characteristics of any symmetric airfoil. The results showed that the proposed model can accurately predict the ice shape under the geometric characteristics of any symmetrical airfoil. The prediction error of the main parameters of the ice shape, such as the ice area and the maximum ice thickness, was kept below 10%.
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