基于神经网络的柴油机韦博参数预测研究

Wiebe Parameters Prediction of Diesel Engine Based on Neural Network

韦博公式作为柴油机零维燃烧模型中较通用的放热率计算半经验公式,被广泛应用于柴油机工作过程的仿真中,其模拟的准确性主要依赖于公式中参数的选取,但常规选取方式存在一定的盲目性且普适性较差。针对这一问题,以上柴D4114B型发电用柴油机为例,提出了一种基于神经网络的柴油机韦博公式参数的预测方法,通过实验所测缸压曲线反推燃烧放热率、放热率曲线的数值拟合和神经网络的训练建立可用于韦博公式参数预测的神经网络模型,通过预测精度评价、预测结果与实验数据的对比,验证了这种预测方式的准确性。最后,基于该方法建立了柴油机的动态仿真模型,通过部分参数实验值与仿真值的比较,证明该方法在柴油机动态仿真中的可行性。

Wiebe formula as a semi-empirical formula that calculates heat release in the zero-dimensional combustion model of diesel engine was widely used in the simulation of working process of diesel engine,the accuracy of simulation was mainly dependent on choice of parameters in the formula,but there were some blindness and poor universality in the traditional Wiebe parameters selection method. So in view of shortcoming of the selection method,the D4114 B type electricity-generating diesel engine was taken as an example,a diesel engine Wiebe formula parameters prediction method was proposed based on neural network. By experimental measurement of cylinder pressure curve,the combustion heat release rate was backward deducted,the heat release rate curve was numerically fitted,and the neural network was trained,a neural network which can be used to predict the parameters of Wiebe formula was established.Through the comparison of predicted results and experimental data, and evaluation of prediction accuracy,the accuracy and feasibility of prediction method were verified. On this basis,D4114 B diesel engine dynamic simulation model was set up by using Modelica language,and the transient performance of diesel engine was simulated and researched. Finally,the simulation results of neural network input parameters were compared with experimental value, which proved the Wiebe formula parameters prediction method can be applied to the diesel engine dynamic simulation study.