| Citation: | REN Yupeng, WANG Qiang, QU Jingguo, et al. Ice shape prediction method for icing wind-tunnel experiment based on transfer learning[J]. Journal of Aerospace Power, 2025, 40（8）：20230169 doi: 10.13224/j.cnki.jasp.20230169
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To address the lack of effective means to predict the ice shape of high-precision wind tunnel experiment, a method combining transfer learning and neural networks was proposed to predict ice shape of wind-tunnel experiment. According to this method, a pre-trained model was obtained by training based on numerical simulation ice shape data samples at first. Secondly, ice shape data samples from icing wind-tunnel experiment were introduced to fine-tune the pre-trained model, ultimately obtaining the final prediction model. The model adopted the U-Net and multilayer perceptron as the main architecture, with airfoil data and icing meteorological parameters as the input, and 2-dimensional ice shape as the output. The results showed that the proposed method can achieve accurate prediction of ice shape in icing wind-tunnel experiment, which was very close to the ice shape in wind-tunnel experiment in terms of main geometric features. The relative error of most results was not more than 15%. This method could provide a new means for studying the characteristics of aircraft icing under ground conditions.
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