基于机器学习的热量流量估计来自高速度视频在和池沸在垂直管的和池期间
Bibhu Bhusan Sha1, Kamalakar Vijay Thakare2, Soumyadipta Kar3
1School of Mechanical Sciences, Indian Institute of Technology Bhubaneswar, Argul, Odisha, 752050, India. a20me09009@iitbbs.ac.in.
Scientific reports
|February 14, 2026
概括
这项研究使用机器学习和高速成像来预测沸热量流. 这种新的方法准确地模拟了泡动态,为热传递分析提供了一种新方法.
科学领域:
- 热力学和流体力学 热力学和流体力学
- 核工程 核工程是指核工程.
- 材料科学 材料科学 材料科学
背景情况:
- 和池的沸对于核电站的安全至关重要.
- 准确预测热流和传热效率对于设备设计和可靠性至关重要.
- 沸涉及泡的形成,生长和离开,受核化场所的影响.
研究的目的:
- 开发一种机器学习框架,用于现场预测沸热量流量.
- 为了将动态气泡的高速成像与热流相关联.
- 为了自动化和提高沸热转移计量学的准确性.
主要方法:
- 利用机器学习方法将高速成像与动态泡行为相关联.
- 采用深度学习模型,包括卷积神经网络和对象检测算法.
- 提取了基于层次和物理的特征,以学习物理沸规律.
主要成果:
- 达到平均热流预测误差约为6%.
- 在所有热量流量级别中保持了88%的整体分类准确度.
- 该模型在统计学上描述了泡核形成,凝聚和离开.
结论:
- 拟议的机器学习框架为传统方法提供了一个基于学习的自动化替代方案.
- 这种方法可以准确地在现场预测沸的热量流.
- 这种方法增强了对沸现象的理解和监测,以提高安全性和设计.
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