Reduced-Order Prediction Method for the Dynamic Thermal Response of Phase-Change Cooling Heat Exchangers

HAN Zeran; TANG Zicheng; KE Hanbing; WANG Junrong; WEI Zhiguo; WANG Qiuwang; MA Ting

doi:10.7652/xjtuxb202606012

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Reduced-Order Prediction Method for the Dynamic Thermal Response of Phase-Change Cooling Heat Exchangers

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

- Reduced-Order Prediction Method for the Dynamic Thermal Response of Phase-Change Cooling Heat Exchangers
- Journal of Xi'an Jiaotong University Vol. 60, Issue 6, Pages: 143-153(2026)
- 作者机构：
  
  西安交通大学能源与动力工程学院，710049，西安
  武汉第二船舶设计研究所，430205，武汉
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202606012
  CLC： TK172
- Received：18 November 2025，
  
  Published：10 June 2026
- 稿件说明：
移动端阅览
HAN Zeran, TANG Zicheng, KE Hanbing, et al. Reduced-Order Prediction Method for the Dynamic Thermal Response of Phase-Change Cooling Heat Exchangers[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 143-153.
DOI：

HAN Zeran, TANG Zicheng, KE Hanbing, et al. Reduced-Order Prediction Method for the Dynamic Thermal Response of Phase-Change Cooling Heat Exchangers[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 143-153. DOI： 10.7652/xjtuxb202606012.

摘要

针对非稳态运行环境下，由高热导率多孔骨架与相变材料组成的相变冷却换热器在动态热响应分析中计算成本高的问题，提出了一种结合本征正交分解与神经网络的高效预测降阶模型。通过焓法模型与多孔介质模型的耦合，数值模拟得到相变冷却换热器温度与液化率数据集，并分析了其储热性能。采用本征正交分解对其进行降阶处理，进一步通过前馈神经网络，实现热流边界与时间向模态系数的非线性映射以及物理场的快速重构。研究结果表明：热流密度为7 W·cm

-2

时，温度的仿真数据与实验结果之间的最大误差为8%。复合多孔石墨显著提升了相变冷却换热器的传热能力。前馈神经网络中，温度与液化率模态系数的预测决定系数分别达到0.999和0.952。降阶重构后的温度与液化率最大绝对误差分别为0.6℃和0.08。与传统模拟方法需耗费数小时相比，该方法在保持计算精度的同时将预测时间缩短至秒级。该研究为相变冷却换热器的高效分析与实时预测提供了一种新思路。

Abstract

In response to the high computational cost associated with dynamic thermal response analysis of phase-change cooling heat exchangers（PCCHEs）composed of porous skeletons with high thermal conductivity and phase-change materials under unsteady operating conditions，an efficient reduced-order prediction model integrating proper orthogonal decomposition（POD）and a feedforward neural network（FNN）was proposed.Temperature and liquid-fraction datasets were generated by numerical simulation through coupling of an enthalpy model and a porous media model，and the thermal storage performance was further

analyzed.POD was employed for order reduction，and an FNN was further trained to establish the nonlinear mapping from heat flux boundary and time to modal coefficients，enabling rapid reconstruction of the physical fields. Results show that，at a heat flux density of 7 W·cm

-2

，the maximum error between simulated and experimental temperatures was 8%.The use of composite porous graphite was found to significantly enhance the heat transfer capability of the PCCHEs.In the FNN，the coefficients of determination for the predicted modal coefficients of temperature and liquid fraction reached 0.999 and 0.952，respectively.After reconstruction from the reduced-order model，the maximum absolute errors of temperature and liquid fraction were 0.6℃and 0.08.Comparedwith conventional simulation methods that take several hours，the proposed method reduced prediction time to the order of seconds while maintaining computational accuracy.The study provides a novel approach for efficient analysis and real-time prediction of PCCHEs.

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