4.7 Article

Modeling and optimization of a light-duty diesel engine at high altitude with a support vector machine and a genetic algorithm

期刊

FUEL
卷 285, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2020.119137

关键词

Diesel engine; High altitude; Engine modeling; Engine optimization; SVM (support vector machine); GA (genetic algorithm)

资金

  1. Yunnan Province Agricultural Joint Special Foundation [2018FG001-096]
  2. Yunnan Provincial Department of Education Scientific Research Fund Project [2018JS337]
  3. Southwest Forestry University Special Research Program [111912]

向作者/读者索取更多资源

In this research, a multi-objective optimization was conducted on a light-duty diesel engine in plateau regions, using a support vector machine to establish a surrogate model between calibration parameters and engine performance parameters. The results showed that the SVM regression model had excellent predictive performance and generalization abilities for accurately predicting the various performance parameters of the diesel engine. The proposed multi-objective optimization method achieved a good comprehensive performance for the diesel engine running in the plateau region, with a simultaneous reduction in brake specific NOx emissions and fuel consumption.
The engine performances and emissions of light-duty diesel engines in plateau regions have attracted more attention due to the upcoming China VI emission regulations for light-duty vehicles. In order to obtain a superior performance for a diesel engine running at high altitude, in this research, multi-objective optimization was conducted in an entire operating region for a light-duty diesel engine operating at an altitude of 1960 m. A support vector machine (SVM) was employed to set up a surrogate model between the calibration parameters and the engine performance parameters. The multi-objective optimization of the fuel consumption and the emissions was carried out using a genetic algorithm with the premise of keeping the same power performance of the original engine within durability constraints and with a minimum smoke limit. The results showed that the SVM regression model had excellent predictive performance and generalization abilities, and that the model could accurately predict the various performance parameters of the diesel engine. The diesel engine running in the plateau region could achieve a good comprehensive performance with the proposed multi-objective optimization method. In comparison with the base engine in the plateau region, a simultaneous reduction of 52.92% for the brake specific NOx emission and 0.67% for the brake specific fuel consumption was achieved, with an acceptable increase of smoke emission.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据