Research on Relative Position Optimization of Cooling Module Based on Machine Learning

The influence of the position parameters of cooling module on the heat dissipation performance of engine cabin is very complex, so it is difficult to find the best combination of the position parameters based on the traditional method. Firstly, the position parameters of the cooling module are sampled by the method of experimental design, and the optimal combination of the position parameters of the cooling module is obtained by establishing the prediction model and global optimization through machine learning. Finally, by comparing the results of the prediction model with the simulation results, it is proved that the method based on the prediction model is feasible in engineering.