大缸径天然气发动机几何压缩比与米勒度协同优化

Collaborative Optimization of Geometric Compression Ratio and Miller Degree of Large-Bore Natural Gas Engine

利用GT-Power建立了某大缸径天然气发动机的一维热力学循环仿真模型,用原机台架试验数据标定,使仿真模型计算准确后,研究了发动机设计参数几何压缩比以及米勒度对发动机性能的影响。以提高发动机热效率和降低排气温度为目标,利用神经网络建模与遗传算法对发动机设计与控制参数进行协同优化。优化结果表明,在保证发动机扭矩输出的条件下,通过对几何压缩比与米勒度的协同优化可以提高指示热效率,降低排气温度,改善发动机性能。

The GT-Power software was used to establish one-dimensional thermodynamic cycle simulation model of natural gas engine with a large bore.The accuracy of simulation model was verified after the calibration based on the engine bench test data.Then the effect of engine geometric compression ratio and Miller degree on engine performance was studied with the model.The neural network modeling and genetic algorithm were used to conduct the collaborative optimization of engine design and control parameters so as to improve the thermal efficiency and reduce the exhaust gas temperature.The results show that it is feasible to increase thermal efficiency,reduce exhaust gas temperature,and improve engine performance by the collaborative optimization of geometric compression ratio and Miller degree without loss of torque output.