摘要
In the modern aviation industry,accurate prediction of complex flow fields is of great significance for optimizing blade design and improving engine performance.Although traditional computational fluid dynamics(CFD)methods can provide highprecision flow field information,they have long calculation time and high resource consumption,making it difficult to meet the rapid response requirements of engineering practice.As an emerging machine learning model,neural networks have gradually become an effective tool for flow field prediction with their powerful nonlinear mapping capabilities and high computational efficiency.This paper aims to combine machine learning technology to construct an efficient and accurate flow field prediction model method,and introduce new theoretical support for the design and optimization of aircraft engines.This paper first explains the basic concepts of machine learning and the shortcomings of current flow field prediction methods.Then,through three cases,the basic principles and application process of neural networks are introduced,including BP neural network,RBF neural network and UNet neural network methods,and the current status and superiority of neural networks in complex flow field prediction are analyzed in detail,providing an important reference for promoting the informatization and intelligent development of the aviation manufacturing industry.
