Anti-Icing System Performance Prediction Using POD and PSO-BP Neural Networks

Abstract

The anti-icing system is important for ice protection and flight safety. Rapid prediction of the anti-icing system’s performance is critical to reducing the design time and increasing efficiency. The paper proposes a method to quickly predict the anti-icing performance of the hot air anti-icing system. The method is based on Proper Orthogonal Decomposition (POD) and Back Propagation (BP) neural networks improved with the Particle Swarm Optimization (PSO) algorithm to construct the PSO-BP neural network. POD is utilized for data compression and feature extraction for the skin temperature and runback water obtained by numerical calculation. A lower-dimensional approximation is derived from the projection subspace, which consists of a set of basis modes. The PSO-BP neural network establishes the mapping relationship between the flight condition parameters (including flight height, atmospheric temperature, flight speed, median volume diameter, and liquid water content) and the characteristic coefficients. The results show that the average absolute errors of prediction with the PSO-BP neural network model on skin temperature and runback water thickness are 3.87 K and 0.93 μm, respectively. The method can provide an effective tool for iteratively optimizing hot air anti-icing system design.

Keywords

• anti-icing system
• BP neural network
• PSO optimization algorithm
• POD
• skin temperature
• runback water