The core idea of deep learning is to process input vectors and take their outputs as training samples, and then use classifiers to predict and modify these input features. When improving the existing diesel engine cooling system, we can use deep learning to solve the problem. Therefore, the purpose of this paper is to improve the design of diesel engine cooling system and improve the working efficiency. In this paper, the experimental method and comparison method are mainly used to build a diesel engine test bench. By setting the inlet water temperature of the diesel engine, the variation rules and influencing factors of the cooling water temperature and flow rate under three temperatures are analyzed. The experimental results show that when the inlet water temperature of the diesel engine is 90 ℃, the cooling water flow always takes the first place, which indicates that the diesel engine has good performance under this condition.

Marquez Julius. Improvement of Diesel Engine Cooling System Based on Deep Learning Algorithm. Kinetic Mechanical Engineering (2020), Vol. 1, Issue 2: 27-35. https://doi.org/10.38007/KME.2020.010204.

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