Research on prediction of vehicle drag coefficient based on machine learning

A diffuser is an advanced aerodynamic improvement device that reduces the drag coefficient of a vehicle by efficiently guiding airflow. To reduce the time required for developing vehicle aerodynamic performance, it was mounted on the sides of the Ahmed model, with the characteristic dimensions of the diffuser serving as design variables and the drag coefficient as the response. The optimal Latin hypercube experimental design method was employed to generate the design of experiments (DOE) matrix. Subsequently, Fluent was used to solve the simulated drag coefficients for each experimental scenario. After the dataset was constructed, it was applied to several machine learning models, including radial basis function (RBF), Extra Trees, extreme gradient boosting (XGBoost), and particle swarm optimization-backpropagation (PSO-BP). The results demonstrated that the PSO-BP model achieved the best prediction accuracy, whereas the RBF model performed the worst. Finally, the Extra Trees algorithm was used to analyze the influence of five design variables on the drag coefficient. The X₁ exerted the highest influence, while X₅ had the least. This demonstrates that the application of machine learning to automobile aerodynamic design is feasible.