Data-Driven Investigation of Flow Boiling Heat Transfer Characteristics in Micro-Channels under Variable Gravity Environment

Ying Taotao; Hu Xianzu; Su Zhangfan; Chen Qi; Pan Ouyu; Li Gen

doi:10.12465/issn.0253-4339.20251108002

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Data-Driven Investigation of Flow Boiling Heat Transfer Characteristics in Micro-Channels under Variable Gravity Environment

更新时间：2026-04-24

- Data-Driven Investigation of Flow Boiling Heat Transfer Characteristics in Micro-Channels under Variable Gravity Environment
- Journal of Refrigeration Pages: 1-12(2026)
- 作者机构：
  
  1.温州市铁路与轨道交通投资集团有限公司温州 325011
  2. 重庆交通大学航空学院重庆 400074
- 作者简介：
- 基金信息：
  
  the National Natural Science Foundation of China(52175257)
- DOI：10.12465/issn.0253-4339.20251108002
  CLC： TB657.5;TK124
- Received：08 November 2025，
  
  Revised：2026-01-17，
  
  Accepted：02 February 2026，
  
  Online First：24 April 2026，
- 稿件说明：
移动端阅览
应涛涛,胡献竹,苏张凡等.数据驱动下的变重力微通道内流动沸腾传热特性研究[J].制冷学报,

Ying Taotao,Hu Xianzu,Su Zhangfan,et al.Data-Driven Investigation of Flow Boiling Heat Transfer Characteristics in Micro-Channels under Variable Gravity Environment[J].Journal of Refrigeration,
应涛涛,胡献竹,苏张凡等.数据驱动下的变重力微通道内流动沸腾传热特性研究[J].制冷学报, DOI：10.12465/issn.0253-4339.20251108002.

Ying Taotao,Hu Xianzu,Su Zhangfan,et al.Data-Driven Investigation of Flow Boiling Heat Transfer Characteristics in Micro-Channels under Variable Gravity Environment[J].Journal of Refrigeration, DOI：10.12465/issn.0253-4339.20251108002.

摘要

针对轨道交通和航空航天设备在变重力环境下的高效散热需求，本文系统开展了微通道内水-乙二醇混合溶液流动沸腾传热特性的实验研究和数据驱动建模。通过搭建基于离心旋转台的变重力实验平台，实现了1.00

~3.16

的变重力环境模拟，获取了质量流速为50~500 kg/（m

·s）、热流密度为100~800 kW/m

工况下的传热系数和临界热流密度数据。实验结果表明：重力加速度对流动沸腾传热具有显著强化作用，当重力从1.0

增至3.16

时，传热系数提升60%~80%，临界热流密度提高20%~35%。在低干度区，重力增强通过减小气泡脱离直径、提高脱离频率强化传热；在中高干度区，重力增强使液膜变薄且分布均匀，并有效延缓干涸现象。其次，与10种经典传热关联式进行对比发现，传统模型在变重力条件下预测误差较大，其中Fang模型表现最优但平均绝对误差仍达9.6%。最后，采用随机森林、支持向量机和极端梯度提升3种机器学习算法建立了传热系数预测算法模型，其中XGBoost模型在全重力范围内的平均绝对误差仅为3.1%，显著优于传统经验关联式。研究成果可为轨道交通和航空航天机载设备冷却系统的优化设计提供理论依据。

Abstract

Objective

To address the demand for high-efficiency heat dissipation in railway transportation and aerospace equipment under variable gravity environments， we systematically investigated the flow boiling heat transfer characteristics of water-glycol mixtures in microchannels and developed data-driven predictive models. Although microchannel flow boiling offers a compact cooling solution， its characteristics under variable gravity are not well understood， and traditional empirical correlations lack prediction accuracy. In this study， we aimed to fill this gap and provide a theoretical basis for optimizing cooling systems for both railway and aerospace applications.

Methods

Both experimental and machine-learning approaches were employed to evaluate flow boiling heat transfer performance. A variable gravity experimental platform based on a centrifugal rotating table was established， capable of simulating gravity environments from 1.00

to 3.16

. The experimental system featured closed-loop circulation with a 200 mm-long， 2 mm-inner-diameter copper microchannel test section. Experiments were conducted across mass fluxes of 50-500 kg/（m

·s）， heat fluxes of 100-800 kW/m

， system pressures of 0.1-0.3 MPa， and inlet subcoolings of 10-30 ℃. T-type thermocouples with ±0.1 ℃ accuracy were used for temperature measurements， while pressure transducers and differential pressure sensors m

onitored system pressures. Three machine learning algorithms—Random Forest （RF）， Support Vector Machine （SVM）， and Extreme Gradient Boosting （XGBoost）—were developed using 80% training and 20% testing data splits with 5-fold cross-validation for hyperparameter optimization.

Results and Discussions

The results demonstrate significant gravity-induced enhancement of flow boiling heat transfer. As gravity increased from 1.00

to 3.16

， the heat transfer coefficient （HTC） improved by 60%-80%， while the critical heat flux （CHF） increased by 20%-35%. In the region of low vapor quality （

0.3）， gravity enhancement reduced bubble departure diameter according to the relationship

∝ g

-0.5

， leading to increased departure frequency and enhanced microlayer evaporation. In the medium quality region （0.3

0.7）， gravity intensification resulted in thinner and more uniform liquid films， with peak HTC values reaching 23 000 W/（m

·K） at 3.16

compared to 14 100 W/（m

·K） at 1.00

. In the high-quality region （

0.7）， hypergravity delayed the onset from

=0.75 to

=0.8. A comparison with ten classical correlations showed that traditional models exhibit large prediction errors under variable gravity， with the best-performing Fang model achieving only a 9.6% mean absolute error （MAE）. In contrast， the XGBoost model achieves an MAE of 3.1% across all gravity conditions， with particularly superior performance at 3.16

（MAE=3.35%） compared to the Fang model （MAE=18.61%）.

Conclusions

This study confirms that gravity is a critical factor in flow boiling heat transfer， significantly enhancing both HTC and CHF through mechanisms such as bubble dynamic optimization and liquid film redistribution. The XGBoost machine-learning model demonstrates superior accuracy in predicting heat transfer performance under variable gravity compared to traditional methods. These findings provide a crucial theoretical basis for the optimal design of cooling systems for railway-vehicle-mounted aerospace airborne equipment that operate in complex gravitational environments.

关键词

Keywords

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