Machine learning analysis for condensation flow heat transfer in mini/ micro-channels
文献类型：期刊论文
| 作者 | He, Huan-Huan4; Li, Wei4; Zhu, Yu4; Tao, Zhi3; Zhao JF(赵建福)1,2 |
| 刊名 | INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 255页码:15 |
| 关键词 | Condensation heat transfer coefficient Machine learning Explainability Mini/micro-channel |
| ISSN号 | 0017-9310 |
| DOI | 10.1016/j.ijheatmasstransfer.2025.127775 |
| 通讯作者 | Li, Wei(weili96@zju.edu.cn) |
| 英文摘要 | Miniature condensers have emerged as an efficient solution for thermal management of compact high-power devices due to their exceptional heat dissipation capability. However, accurate prediction of heat transfer coefficient(HTC) remains challenging due to complex flow and thermal behaviors in two-phase heat transfer. This study employed explainable machine learning to develop condensation HTC prediction models in mini/micro channels. A multidimensional feature database containing 4003 experimental data points across 19 fluids in hydraulic diameter 0.1mm <= D <= 4.8 mm was constructed. Four machine learning models, including Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), were developed utilizing the database to explore their potential in predicting condensation HTC. The models were validated through internal and external datasets, with comparison against six traditional correlations. The SHapley Additive exPlanations (SHAP) method was subsequently applied to explain the XGBoost prediction mechanism. Results demonstrate all machine learning models achieved satisfactory performance compared to traditional correlations, with XGBoost exhibiting optimal accuracy and generalization. It attained a coefficient of determination (R2) of 0.993 and a mean absolute relative deviation (MARD) of 3.6 % across the database, with strong generalization even for new fluid datas. SHAP explanation revealed Froude number and dimensionless vapor velocity were critical features, while the influence of features such as thermal conductivity and mass flux on the model's prediction aligned with the trend of physical laws and experimental results, effectively enhancing predictive rationality of "black-box" models. This work shows machine learning's significant potential for two-phase heat transfer prediction, providing an efficient predictive tool for mini/microchannel condenser design. |
| 分类号 | 一类 |
| WOS关键词 | FRICTIONAL PRESSURE-DROP ; HORIZONTAL SMOOTH TUBE ; TRANSFER COEFFICIENT ; CO2 ; REFRIGERANTS ; R1234ZE(E) ; R410A ; PREDICTION ; SQUARE ; R134A |
| 资助项目 | Na-tional Natural Science Foundations of China[52320105001] ; National Key Research and Development Program of China[2022YFB3404600] ; Key R & D Project of Shandong Province in China[2023CXPT075] ; Space Application System of China Manned Space Program[KJZ-YY-NLT0505] ; Space Application System of China Manned Space Program[LT3-10] |
| WOS研究方向 | Thermodynamics ; Engineering ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001570461100006 |
| 资助机构 | Na-tional Natural Science Foundations of China ; National Key Research and Development Program of China ; Key R & D Project of Shandong Province in China ; Space Application System of China Manned Space Program |
| 其他责任者 | Li, Wei |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/103871]  |
| 专题 | 力学研究所_国家微重力实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, CAS Key Lab Micrograv, Beijing 100190, Peoples R China; 3.Beihang Univ, Res Inst Aeroengine, Natl Key Lab Sci & Technol Aeroengine Aerothermody, Beijing, Peoples R China; 4.Zhejiang Univ, Dept Energy Engn, Hangzhou 310027, Peoples R China; |
| 推荐引用方式 GB/T 7714 | He, Huan-Huan,Li, Wei,Zhu, Yu,et al. Machine learning analysis for condensation flow heat transfer in mini/ micro-channels[J]. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER,2026,255:15. |
| APA | He, Huan-Huan,Li, Wei,Zhu, Yu,Tao, Zhi,&赵建福.(2026).Machine learning analysis for condensation flow heat transfer in mini/ micro-channels.INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER,255,15. |
| MLA | He, Huan-Huan,et al."Machine learning analysis for condensation flow heat transfer in mini/ micro-channels".INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 255(2026):15. |
入库方式： OAI收割
来源：力学研究所
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