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使用ANN和RSM进行SI发动机性能和排放与GEM混合物的预测建模和优化

Farooq Shaik¹, D Vinay Kumar¹, N Channa Keshava Naik²

¹Department of Mechanical Engineering, Vignan's Foundation for Science Technology and Research, Vadlamudi, Andhra Pradesh, India.

Scientific reports

|February 7, 2025

概括

这项研究使用人工神经网络 (ANN) 和响应表面方法 (RSM) 来优化使用汽油,乙醇和甲醇 (GEM) 燃料混合物的单SI发动机. 最佳的E20混合物实现了高效率和低排放.

科学领域:

内部燃烧发动机内部燃烧发动机
其他替代燃料替代燃料
计算建模计算建模

背景情况:

优化发动机性能和排放量对于燃油效率和环境影响至关重要.
汽油,乙醇和甲醇 (GEM) 混合物为内燃机提供了替代燃料的潜力.
预测建模和优化技术可以加速开发高效的燃料混合物.

研究的目的:

使用GEM混合物预测单SI发动机的性能和排放.
优化发动机运行条件,以获得最大的性能和最小的排放量.
评估人工神经网络 (ANN) 和响应表面方法 (RSM) 在引擎优化中的有效性.

主要方法:

开发了一个人工神经网络 (ANN) 模型来预测发动机性能和排放.
使用响应表面方法 (RSM) 来优化发动机参数和燃料混合.
进行了实验验证,以评估ANN预测的准确性.

主要成果:

在预测发动机性能和排放方面,ANN模型的误差低于5%.
最佳运行条件被确定为2992.9rpm的发动机转速和E20相当的GEM混合物.
在优化的条件下,制动热效率 (B_The) 为34.63%,BSFC为243.7 g/kW-hr,排放量低 (1.5% CO,108.13 ppm HC,1211.8 ppm NOx).

结论:

通过使用替代燃料混合物,ANN和RSM是优化发动机性能的有效工具.
与E20相当的GEM混合物在发动机效率和排放之间提供了有利的平衡.
该研究表明,对于优化发动机设置来说,性能和排放特征的组合非常理想.

关键词:

一个年龄,一个年龄.排放量排放量排放量排放量相当于GEM混合物的混合物优化优化优化优化业绩表现业绩表现业绩表现预测预测预测在 RSM RSM 中.

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