联合神经与遗传算法的发动机进气管参数优化

Optimization of engine intake pipe parameters by combining neuraland genetic algorithms

以进气管结构参数为研究对象,联合神经与遗传的集成优化算法,提升发动机动力与经济性能。通过搭建发动机的一维性能仿真模型,结合外特性性能曲线对模型边界参数进行了修正,研究了进气管长度和管径对发动机性能的量化影响;进一步基于神经网络遗传算法分析了适用于不同工况的进气管最优参数。结果表明,在发动机4 000~5 500 r/min转速范围,扭矩提升明显,5 000~7 000 r/min转速范围,比油耗降低明显。在所设定的优化目标下,扭矩优化率最高提升12.08%,比油耗降低1.51%。联合一维系统仿真模型和神经网络遗传算法,对进气管结构参数进行优化,系统集成方法可以为可变进气参数设置提供数据基础。

Taking the structural parameters of an intake pipe as the research object,this paper proposes an integrated optimization by combining neural and genetic algorithms to improve the power and economic performance of the engine.By building a one-dimensional performance simulation model of the engine,the boundary parameters of the model are modified in combination with the external characteristic performance curve,and the quantitative effects of the length and diameter of the intake pipe on the engine performance are studied.Further,based on the neural network genetic algorithm,the optimal parameters of the intake pipe applicable to different working conditions are analyzed.The results show that the torque increases significantly in an engine speed range of 4000 to 5500 r/min,while the specific fuel consumption reduces significantly in an engine speed range of 5000 to 7000 r/min.Under the optimization target set in this paper,the maximum torque optimization rate increases by 12.08%and the specific fuel consumption reduces by 1.51%.Based on the one-dimensional system simulation model and the neural network genetic algorithm,the structural parameters of the intake pipe are optimized.The system integration method can provide data basis for the setting of variable intake parameters.