哈尔滨工程大学学报2024，Vol.45Issue(7)：1322-1329,8.DOI:10.11990/jheu.202207032
零维预测燃烧模型建模方法
Modeling method of the zero-dimensional predictive combustion model
摘要
Abstract
To solve the problem that the zero-dimensional predictive combustion model of diesel engines established using the neural network algorithm is unstable in predicting steady-state and dynamic operating conditions,this study uses the genetic algorithm to comprehensively optimize the initial weights and thresholds of the neural network and proposes a genetic-neural network algorithm.First,based on the TBD620 diesel engine,the operating parameters and cylinder pressure data were obtained by steady-state and transient tests.Then,the corresponding combustion parame-ters were obtained by algebraic analysis combined with the genetic algorithm.Finally,the combustion models were constructed using the genetic-neural network algorithm and the neural network algorithm,and the identification results were compared.Results showed that,compared with the neural network algorithm,the zero-dimensional predictive combustion model constructed by the genetic algorithm-neural network algorithm reduces the average error of the pre-dicted values of θ50 and IMEP by 43.84%and 42.73%,respectively.The genetic algorithm has a high-efficiency ca-pability to optimize the weight and threshold.The model has higher prediction accuracy and better generalization,which is suitable for the study of the zero-dimensional predictive combustion model of diesel engines.关键词
柴油机/韦伯方程/零维燃烧模型/神经网络/遗传算法/生物柴油/代数分析法/遗传算法-神经网络算法Key words
diesel engine/Weber function/zero-dimensional predictive combustion model/neural network/genetic algorithm/biodiesel/algebraic analysis method/genetic-neural network algorithm分类
交通工程引用本文复制引用
胡登,王贺春,王彬彬,王银燕,杨传雷,史明伟..零维预测燃烧模型建模方法[J].哈尔滨工程大学学报,2024,45(7):1322-1329,8.基金项目
国家自然科学基金项目(52171298) （52171298）
博士研究生科研创新基金项目(3072023GIP0303). （3072023GIP0303）
