柴油机Wiebe模型参数优化及燃烧性能预测  

Parameter Optimization and Combustion Performance Prediction of Diesel Engine-Based Wiebe Model

作　　者：张帆[1] 马庆国 王子玉 曹如楼 李超凡 裴毅强[1] Zhang Fan;Ma Qingguo;Wang Ziyu;Cao Rulou;Li Chaofan;Pei Yiqiang(State Key Laboratory of Engines(Tianjin University),Tianjin 300072,China;China North Engine Research Institute,Tianjin 300400,China)

机构地区：[1]先进内燃动力全国重点实验室(天津大学),天津300072 [2]中国北方发动机研究所,天津300400

出　　处：《天津大学学报（自然科学与工程技术版）》2024年第5期473-481,共9页Journal of Tianjin University：Science and Technology

基　　金：中国北方发动机研究所国防科技重点实验室基金资助项目(6142212200410).

摘　　要：基于一台单缸柴油机进行了发动机性能实验,通过结合单、双Wiebe燃烧模型和机器学习算法,提出了一种可预测的Wiebe燃烧模型,开展了不同边界条件下的燃烧参数和规律预测研究.首先,使用代数化Wiebe方程的线性拟合,根据线性拟合精度选取单、双Wiebe模型.然后,使用列文伯格-马夸尔特(Levenberg-Marquardt,LM)算法拟合Wiebe方程得到相应的6个Wiebe参数,实现放热率Wiebe参数化.最后,基于该Wiebe燃烧参数,应用误差反向传播神经网络(back propagation neural network,BP-NN)和随机森林(random forest,RF)算法,开发了实用性更广泛的两种Wiebe燃烧预测模型,研究了不同边界条件下的燃烧规律.结果显示:代数Wiebe方程的线性拟合精度小于等于0.99000时放热率曲线更复杂,此时选用双Wiebe方程可得到高精度的Wiebe燃烧参数,反之选用单Wiebe方程即可;在1200 r/min和2200 r/min时选择双Wiebe方程对放热率进行拟合,拟合精度R^(2)均大于0.99000,误差平方和均小于0.01,通过Wiebe参数重新构建的放热率和实验放热率基本一致.基于LM算法的放热率拟合算法,可以很好地反映柴油机不同工况下的燃烧特征.对比两种不同的燃烧预测模型BP-NN和RF发现:BP-NN模型对一Wiebe形状因子m1和一Wiebe燃烧初始相位φ_(01)的预测精度更高,而RF算法对一Wiebe燃烧比例α和燃烧结束相位φ_(end)的预测精度更高,因此,针对不同燃烧参数选择不同预测模型可以有效提高Wiebe燃烧预测模型的精度.In this study,based on the experimental performance study of a single-cylinder diesel engine,a predictable Wiebe combustion model was proposed,including the selection of single and double Wiebe combustion models and machine learning algorithms.Herein,combustion parameters and engine performances of single-cylinder engine under different boundary conditions were also predicted.First,the single or double Wiebe model was selected based on how well the algebraic Wiebe equation fitted.Then,six Wiebe parameters were determined by further fitting the Wiebe equation with the Levenberg-Marquardt(LM)algorithm to model the heat release rate.Finally,using error back-propagation neural network(BP-NN)and random forest(RF)algorithms,two Wiebe combustion prediction models with greater applicability than Wiebe equation were developed,and combustion characteristics under different boundary conditions were investigated for single-cylinder engine.Results show that when the linear fitting accuracy of the algebraic Wiebe equation is less than or equal to 0.99000 with a complex heat release rate curve,the double Wiebe equation is selected to obtain high-precision Wiebe combustion parameters.In contrast,the single Wiebe equation can be used.The values of fitting accuracy R^(2)of the heat release rate on the double Wiebe equation at 1200 and 2200 r/min are greater than 0.99000,and the values of sum of squared errors are less than 0.01.Moreover,the heat release rate reconstructed by Wiebe parameters agrees with the experiment.The heat release rate fitting algorithm based on the LM algorithm can well reflect the combustion characteristics of diesel engines under different operating conditions.Furthermore,the BP-NN combustion prediction model shows higher accuracy in the first Wiebe shape factor(m1)and first Wiebe combustion initial phase(φ_(01)),while the RF algorithm exhibits higher accuracy in the first Wiebe combustion proportion(α)and combustion end phase(φ_(end)).Therefore,selecting an appropriate prediction model for different com

关 键 词：柴油机 Wiebe燃烧模型 列文伯格-马夸尔特算法 神经网络 随机森林算法 

分 类 号：TK421.2[动力工程及工程热物理—动力机械及工程]