Prediction of Aircraft Wake Vortices under Various Crosswind Velocities Based on Convolutional Neural Networks

Abstract

In order to overcome the time-consuming computational drawback of using computational fluid dynamics (CFD) for the numerical simulation of aircraft wake vortex evolution under different crosswind velocities, this paper proposes a wake vortex prediction model based on a convolutional neural network (CNN) algorithm. The study focuses on the B737-800 aircraft, and employs CFD numerical simulations to obtain the evolutionary characteristics of wake vortex parameters under crosswind velocities ranging from 0 to 7 m/s. The wake vortex velocity and Q-criterion vorticity values are collected and partitioned into mutually exclusive training and testing datasets. A CNN model is constructed, and the training dataset is used to tune hyperparameters to minimize loss and achieve accurate predictions. After saving the trained model, the desired crosswind velocity value is input to obtain the predicted wake vortex velocity and Q-criterion vorticity values. The results indicate that the convolutional neural network model exhibits an average absolute percentage error of 1.5%, which is 2.3% lower than that of the fully connected neural network model. This suggests that convolutional neural networks can enhance the accuracy of wake vortex predictions, as demonstrated in this study. Compared to traditional CFD methods, the proposed model reduces the computation time by approximately 40 times, effectively improving computational efficiency and offering valuable insight for studies involving numerous numerical simulations, such as analyzing the safety separation between aircraft wake vortices during paired approach procedures.