重庆理工大学学报2025，Vol.39Issue(5)：225-234,10.DOI:10.3969/j.issn.1674-8425(z).2025.03.028
天然气发动机燃烧放热的预测研究
Prediction of the combustion heat release in a natural gas engine
摘要
Abstract
To investigate the impact of excess air coefficient(λ)on the combustion heat release characteristics of natural gas engine under low load conditions,we conduct experiments on an electronically controlled multi-point sequential injection natural gas engine and collect data.Three combustion heat release prediction models are built by integrating the genetic algorithm(GA)with the backpropagation(BP)neural network:the standard BP neural network model,the GA-optimized BP neural network model,and the double-stage GA-optimized BP neural network model.The predicted outcomes from each model are evaluated and compared with our experimental results.Our findings indicate the three prediction models deliver impressive predictive performances.Compared to the standard BP neural network model and the traditionally GA-optimized BP neural network model,the DSGA-optimized BP neural network prediction model achieves the most accurate prediction for heat release rate(HRR),cumulative heat release(HR),and combustion reaction rate(CRR).Under the lean burn limit(λ=1.9)condition,the DSGA-optimized model still accurately predictes the heat release pattern during the early and middle stages of the combustion cycle,although its prediction accuracy decreases in the later stages.关键词
天然气发动机/燃烧放热/双阶段优化/遗传算法/神经网络Key words
natural gas engine/combustion heat release/double-stage optimization/genetic algorithm/neural network分类
能源科技引用本文复制引用
崔瀚林,刘庆伟,丁顺良,高建设,宋恩哲..天然气发动机燃烧放热的预测研究[J].重庆理工大学学报,2025,39(5):225-234,10.基金项目
国家自然科学基金项目(51906225) （51906225）
船舶动力工程技术交通运输行业重点实验室(武汉理工大学)开放基金项目(KLMPET2021-05) （武汉理工大学）
教育部"春晖计划"合作科研项目(HZKY20220281) （HZKY20220281）
中国国家留学基金项目(202308410392) （202308410392）
