基于神经网络的费托燃油发动机特性研究  

Research on FT fuel engine characteristics using neural network

作　　者：武涛 张武高[2] 彭海勇 张海波 缪雪龙 WU Tao;ZHANG Wugao;PENG Haiyong;ZHANG Haibo;MIAO Xuelong(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Key Laboratory for Power Machinery and Engineering of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620 [2]上海交通大学动力机械与工程教育部重点实验室,上海200240

出　　处：《上海工程技术大学学报》2023年第1期68-75,共8页Journal of Shanghai University of Engineering Science

基　　金：上海市科委重点科技攻关项目资助(043012015);上海市高校选拔培养优秀青年教师科研专项基金项目资助(06XPYQ20)。

摘　　要：基于反向传播(Back Propagation,BP)神经网络强大的非线性逼近和自学习能力,设计3层网络模型,采集发动机台架试验数据作为样本进行模型训练和检验.以发动机转速、转矩、供油提前角和以天然气为原料的费托燃油(GTL)与柴油混合燃料特性参数十六烷值、硫含量、芳香烃含量为输入,建立BP神经网络模型预测GTL发动机特性.结果表明,采用该模型可同时预测GTL发动机功率、油耗、排温、HC、CO、CO_(2)、NO_(x)和碳烟排放等特性;与试验数据对比,预测结果的相对误差基本在5%以内,表明该模型具有较高的模型精度和良好的泛化能力.Based on the strong nonlinear approximation and self-learning ability of Back Propagation(BP)neural network,a three-layer network model was designed,and the engine bench test data were collected as the samples to train and verify the model.The engine speed,torque,fuel supply advance angle and the characteristic parameters of the fuel mixture of Fischer-Tropsch gas-to-liquid(GTL)and diesel,such as cetane number,sulfur content and aromatic hydrocarbon content,were taken as inputs.The BP neural network model was established to predict the characteristics of GTL engine.The results show that the model can predict the power,fuel consumption,exhaust temperature,HC,CO,CO_(2),NO_(x) and soot emission of GTL engine at the same time.Compared with the experimental data,the relative error of the prediction results is nearly within 5%,which shows that the model has high model accuracy and good generalization ability.

关 键 词：BP神经网络 费托燃油 发动机 排放 模型 

分 类 号：TK427[动力工程及工程热物理—动力机械及工程]