随机森林方法用于估计车特定燃油消耗.
Qinsheng Yun1,2, Xiangjun Wang3, Chen Yao4
1Naval University of Engineering, Wuhan, 430000, China. yunqinsheng@126.com.
Scientific reports
|October 18, 2023
概括
准确估计车特定燃油消耗 (BSFC) 地图对于内燃机至关重要. 改进的随机森林方法提高了BSFC地图估计的准确性,优于现有技术.
科学领域:
- 工程 工程师 工程师 工程师
- 热力学是一种热力学.
- 数据科学数据科学数据科学
背景情况:
- 内燃机是关键的动力来源,其能量效率以制动特定燃油消耗 (BSFC) 来衡量.
- BSFC 地图对于发动机分析和优化至关重要.
- 当前的BSFC地图估计方法,如K-最近邻居和多层感知子,存在精度限制,特别是在稀疏数据的情况下.
研究的目的:
- 开发一种更准确的方法来估计车特定燃油消耗 (BSFC) 地图.
- 改进现有的处理分布式采样数据的技术,用于BSFC地图生成.
主要方法:
- 为BSFC估计提出了一种改进的随机森林方法.
- 多项式特征被用于非线性转换以增加特征尺寸.
- 使用粒子群优化算法优化随机森林模型的关键参数.
主要成果:
- 提议的改进随机森林方法与常见的估计技术相比,表现优越.
- 该方法在两个数据集中估计了20%,30%和40%的BSFC数据的有效性.
- 改进的方法提供了更准确的BSFC地图估计,特别是分布式采样数据.
结论:
- 改进的随机森林方法是BSFC地图估计的高效方法.
- 这种技术比传统的内燃机分析方法有了显著的进步.
- 提出的方法适用于准确的BSFC地图生成,解决当前方法的局限性.
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