Advances in the Application of Machine Learning to Boiling Heat Transfer

CHU Wenxiao; TANG Weifeng; BI Xiaolong; ZHOU Chenjie; SURTAEV Anton Sergeevich; WANG Qiuwang; PAVLENKO Alexander Nikolaevich

doi:10.7652/xjtuxb202606010

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Advances in the Application of Machine Learning to Boiling Heat Transfer

Special Topic Multi-Scenario Heat Transfer | 更新时间：2026-06-12

- Advances in the Application of Machine Learning to Boiling Heat Transfer
- Journal of Xi'an Jiaotong University Vol. 60, Issue 6, Pages: 118-131(2026)
- 作者机构：
  
  西安交通大学热流科学与工程教育部重点实验室，710049，西安
  俄罗斯科学院库塔拉泽热物理研究所，630090，俄罗斯新西伯利亚
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202606010
  CLC： TK124
- Received：04 October 2025，
  
  Published：10 June 2026
- 稿件说明：
移动端阅览
CHU Wenxiao, TANG Weifeng, BI Xiaolong, et al. Advances in the Application of Machine Learning to Boiling Heat Transfer[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 118-131.
DOI：

CHU Wenxiao, TANG Weifeng, BI Xiaolong, et al. Advances in the Application of Machine Learning to Boiling Heat Transfer[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 118-131. DOI： 10.7652/xjtuxb202606010.

摘要

沸腾传热广泛存在于锅炉水冷壁、核反应堆蒸发器等能源动力装备，是系统高效运行的关键，对其进行准确预测可有效避免装备局部过热、蒸干、超温等问题。传统预测模型存在难以突破复杂多物理场耦合的瓶颈，而机器学习方法可通过数据驱动建模和智能分析，为解决该问题提供新思路。首先，综述了人工智能技术在沸腾传热预测中的应用，总结了近年来采用机器学习算法在传热系数预测、气泡动力学参数提取等方面的工作，结果表明虽然人工智能在沸腾传热中展现出显著优势，但仍面临数据依赖性高、模型“黑盒”特性强和计算资源需求大等挑战。然后，阐述了机器学习在沸腾传热中的未来研究方向，包括但不限于快速及低成本的多相流仿真、跨场景的泛化模型开发和传热系统的实时动态控制。最后，指出融合物理定律与数据驱动、构建数据集及开源算法将成为推动人工智能在沸腾过程深度应用的关键，从而进一步助力高效能源动力系统开发。该研究可为机器学习方法在沸腾传热过程中的智能预测、优化设计与安全控制提供参考。

Abstract

Boiling heat transfer，widely applied to energy and power equipment，such as boiler water-cooled walls and nuclear reactor evaporators，is critical to their efficient operation.Accurate prediction of boiling heat transfer is therefore essential to prevent such equipment from local overheating，dry-out，over-temperature，and other abnormalities effectively.While traditional prediction models have been constrained by the challenge of coupling complex multi-physical fields，machine learning offers new ideas for addressing this challenge through data-driven modeling and intelligent analysis.First of all，the application of artificial intelligence（AI）techniques to boiling heat transfer prediction is reviewed，with recent years of work using machine learning algorithms for heat transfer coefficient prediction and bubble dynamics parameter extraction summarized.The results indicate that，although notable advantages of AI in boiling heat transfer have been demonstrated，challenges remain，including strong dependence on data，obvious “black box”nature of models，and substantial computational resource requirements.Furthermore，future research directions for machine learning in boiling heat transfer are identified，including but not limited to rapid and lowcost multiphase flow simulation，development of generalized models across different scenarios，and real-time dynamic control of heat transfer systems.Finally，it is promisingly expected that the integration of physical laws with data-driven modeling，together with the building of datasets and the provision of open-source algorithms，will be pivotal in deepening the application of AI to boiling processes，further empowering the development of efficient energy and power systems. This study can provide a reference for the application of machine learning methods to intelligent prediction，optimal design，and safety control of boiling heat transfer processes.

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references

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