Prediction of icing wind tunnel temperature field with machine learning

Icing wind tunnel is an important infrastructure for research on aircraft icing and anti-deicing in which refrigeration system realizes precise control of the airflow temperature in the wind tunnel by adjusting the suction pressure of the compressor unit. The suction pressure control and cooling methods affect the wind tunnel test efficiency. In this paper, aiming at accurate prediction of compressor suction pressure, the support vector regression （APSO–SVR） optimized by adaptive particle swarm algorithm is used to establish pressure prediction model to conduct pressure prediction and experimental research. In order to further improve the efficiency of icing wind tunnel testing, multi-layer perceptron （MLP） neural network is used to establish an analysis model to analyze the influence of test parameters on the cooling rate of wind tunnel. The results show that the average absolute percentage error （E_MAP） between the predicted and test value of the compressor suction pressure is less than 4%, and the mean square error （E_MS） is less than 0.003; the parameters affecting the wind tunnel cooling rate are mainly airflow density, test wind speed, compressor suction pressure and the initial temperature of the heat exchanger outlet. Among them, the compressor suction pressure has the most significant effect on it.