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预测浮力喷气机的特性:一种机器学习方法
Hossein Hassanzadeh1, Saptarshi Joshi1, Seyed Mohammad Taghavi1
1Department of Chemical Engineering, Université Laval, Québec, QC, G1V 0A6, Canada.
概括
我们使用机器学习来预测浮力混合喷气的特征,发现随机森林算法最准确地预测了层状长度和传播角度.
科学领域:
- 流体动力学 流体动力学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 积极浮动的混合喷气在各种工业和环境过程中至关重要.
- 精确预测喷气体特性,如层状长度和扩散角度,对于建模和控制至关重要.
研究的目的:
- 采用监督机器学习技术来预测可混合喷气的关键特性.
- 将不同机器学习算法的性能与经验相关性进行比较.
主要方法:
- 高速成像和平面激光诱导的光被用于数据采集.
- 使用监督机器学习算法,包括线性回归,支持向量回归,随机森林,K-最近邻居和人工神经网络.
- 模型性能使用标准指标进行评估.
主要成果:
- 与其他测试模型相比,随机森林算法在预测喷气特性方面表现优越.
- 随机森林模型显著优于最近的经验相关性,特别是在预测层状长度方面.
- 通过广泛的雷诺兹和阿基米德数来实现准确的预测.
结论:
- 监督机器学习,特别是随机森林算法,提供了一个非常准确的方法来预测可混合喷气体的特性.
- 这种方法比传统的经验相关性提供了显著的改进,特别是在层状长度预测方面.
- 这些发现对增强浮力喷气现象的建模和控制有意义.
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