| Citation: | Jie Yang, Qian Yang, Pengcheng Chen, et al. Rapid prediction of anti-icing surface temperature and runback water via point-cloud slice attention network[J]. Journal of Aerospace Power, 2026, 42（X）：20260196 doi: 10.13224/j.cnki.jasp.20260196
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To evaluate the performance of engine inlet hot-air anti-icing systems under different structural designs, a mesh-independent point-cloud slice attention network (PSANet) was developed for rapid pointwise prediction of the surface temperature and runback water distribution over the inlet. In this method, the anti-icing surface was represented as a point cloud, and six-dimensional structural design parameters of the piccolo tube were used as inputs. Multi-band Fourier feature mapping, conditional modulation, slice attention, and vector-gated reconstruction were introduced, and a multi-task learning strategy was adopted to jointly predict the temperature distribution and the mass flow rate distribution of runback water. Based on an in-house numerical simulation method for anti-icing, 5 000 cases of anti-icing surface temperature and runback water mass flow rate under different piccolo-tube structural designs were generated. These cases were randomly divided into training, validation, and test sets, with each case containing 23 958 surface points. The test results show that the mean absolute error and root mean square error for temperature prediction were 0.503 K and 0.841 K, respectively; the mean absolute error and root mean square error for runback water mass flow rate prediction were 0.011 mg/s and 0.018 mg/s, respectively. The prediction time for a single case was 38.57 ms, representing an acceleration of approximately 6, 880 times compared with the anti-icing numerical simulation method. Overall, the PSANet method achieves high prediction accuracy and computational efficiency, and can be applied to sensitivity analysis of structural design parameters and iterative design of hot-air anti-icing systems.
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