Research on aerodynamic attachments parameter optimisation by integrating BP neural network and genetic algorithm

In this study, aerodynamic attachments at the rear of a vehicle are optimised using CFD simulation and Latin hypercube design to reduce drag and improve high speed performance. Models are constructed based on four design variables and simulated using ANSYS fluent and realisable k-epsilon models. Neural networks and genetic algorithms were combined to find the optimal solution, resulting in a drag reduction of more than 11.7%. The study also analyses the effect of each variable on drag using random forest and verifies the reliability of the results. Ultimately, it provides a new approach to optimising the aerodynamic performance of vehicles, helping to reduce energy consumption.