The potential of machine learning to predict melting response time of phase change materials in triplex-tube latent thermal energy storage systems

Aug 6, 2026

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Accurate prediction of melting response time is vital for optimizing thermal energy storage systems, which help reduce the temporal mismatch between thermal energy demand and supply in the built environment. This study quantitatively predicts the melting response time of a triplex-tube latent thermal energy storage system using phase change materials and Y-shaped fins to enhance heat transfer. An enthalpy-porosity based numerical model generates a dataset of 60 cases with response times from 15 to 45 min under varying design and operating conditions. Variable independence is validated, then four machine learning algorithms (polynomial regression, SVR, random forest, XGBoost) are trained with Bayesian hyperparameter optimization; XGBoost achieves 92% accuracy. Feature importance shows fin width and heat transfer fluid temperature dominate (51% and 47%), while fin angle contributes only 2%.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Highlights

Y-shaped fins and charging performance

Four machine learning methods for prediction

Performance evaluation and feature importance

Article information and abstract

Introduction

Climate warming challenge and renewable energy demand

Thermal energy storage to address temporal mismatch

The potential of machine learning to predict melting response time of phase change materials in triplex-tube latent thermal energy storage systems Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open Access Yan, P., Wen, C. ORCID: [https://orcid.org/0000-0002-4445-](https://orcid.org/0000-0002-4445-)[ ](https://orcid.org/0000-0002-4445-)[1589](1589) , Ding, H. , Wang, X. and Yang, Y. (2025) The potential of machine learning to predict melting response time of phase change materials in triplex-tube latent thermal energy storage systems. Applied Energy, 390. 125863. ISSN 1872-9118 doi: 10.1016/j.apenergy.2025.125863 Available at [https://centaur. reading.ac. uk/122526/](https://centaur. reading.ac. uk/122526/) It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing. To link to this article DOI: [http://dx.doi.org/10.1016/j.apenergy.2025.125863](http://dx.doi.org/10.1016/j.apenergy.2025.125863) Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement . [www. reading.ac. uk/centaur](www. reading.ac. uk/centaur) CentAUR Central Archive at the University of Reading Reading’s research outputs online Applied Energy 390 (2025) 125863 Contents lists available at ScienceDirect Applied Energy journal [homepage:](homepage: www.elsevier.com/locate/apenergy)[ www.elsevier.com/locate/apenergy](homepage: www.elsevier.com/locate/apenergy) | The potential of machine learning to predict melting response time of phase change materials in triplex-tube latent thermal energy storage systems☆
Peiliang Yana, Chuang Wen b,*, Hongbing Ding c, Xuehui Wang d, Yan Yang e,*
a School of Energy and Power Engineering, Beihang University, Beijing 100191, China
b School of the Built Environment, University of Reading, Reading RG6 6AH, United Kingdom c School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China d School of Mechanical & Materials Engineering, University College Dublin, Dublin D04 V1W8, Ireland e Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4QF, United Kingdom | | | | --- | --- | --- | | H I G H L I G H T S | | | | • Y-shaped fins to enhance phase change material charging performance in latent thermal energy storage systems.
• Predicting melting response time using four machine learning methods.
• Evaluation of model performance using mean square error and coefficient of determination.
• Conducted Feature importance evaluation to guide fin improvements. | | | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Phase change material Thermal energy storage Machine learning Melting response time XGBoost algorithm | | Accurate prediction of the melting response time is vital for optimizing thermal energy storage systems, which play a key role in addressing the temporal mismatch between thermal energy demand and supply in the built environment. This study aims to quantitatively predict the melting response time of a novel triplex-tube thermal energy storage system incorporating phase change materials and Y-shaped fins to enhance heat transfer. A numerical model based on the enthalpy-porosity method was developed to simulate the melting process, resulting in a dataset comprising 60 cases with melting response times ranging from 15 to 45 min under varying design and operational conditions. The key parameters investigated include fin angle (10◦–30◦), fin width (5–15 mm), and heat transfer fluid temperature (60 ◦ C–80 ◦ C). Prior to model development, variable independence was validated to ensure robust predictions. Four machine learning algorithms—polynomial regression, support vector regression, random forest regression, and extre

FAQs

What does the study predict and why is it important?

The study predicts the melting response time of phase change materials in a triplex-tube latent thermal energy storage system. It supports optimization by addressing the temporal mismatch between thermal energy demand and supply in buildings.

How was the dataset for model training generated?

An enthalpy-porosity numerical model simulates the melting process and produces 60 cases. Melting response times range from 15 to 45 minutes across different design and operating conditions.

Which machine learning method performed best, and how accurate was it?

The XGBoost model provided the best predictive capability, reaching 92% accuracy. Its results outperformed the other three algorithms tested.

Which design/operating parameters most influence melting response time?

Feature importance analysis indicates fin width and heat transfer fluid temperature dominate the prediction variance, contributing 51% and 47%, respectively. Fin angle has a marginal influence of about 2%.

Aug 6, 2026

Upload by Eliana