The investigation of super-cooled droplet impingement characteristics is the most important step for aircraft icing and anti-icing/de-icing analyses. The Lagrangian method and the Eulerian method are widely used to compute the droplet motion and collection efficiency, and the two methods are considered to obtain almost the same results for surface impingement characteristics under icing conditions. The models and implementation approaches of the two methods were established in this work, and the simulations of droplet motion were carried out for a NACA 0012 airfoil, a 2D section of an A320 head, a multi-element airfoil, and an icing wind tunnel. The collection efficiencies of the NACA 0012 airfoil obtained by the present Lagrangian and Eulerian methods show good agreement with the results in the literature, validating the established methods. The droplet impingement characteristics of the two methods are consistent for the aircraft surfaces without upstream trajectory deflections. However, when the droplet motion is deflected by the frontal body before hitting the rear surfaces, the results obtained by the two methods are different whether the droplet trajectories intersect or not, which subverts the traditional opinion that the Lagrangian and Eulerian methods would obtain the same result of the droplet impingement characteristics. The reason is studied in detail according to the droplet motion results in the icing wind tunnel. The findings of this work are helpful for the accuracy of aircraft icing and anti-icing/de-icing simulations, and useful for the development of airworthiness certification.

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.