https://doi.org/10.1140/epjp/s13360-025-06751-w
Regular Article
Experimental and machine learning approaches to predict the heat transfer in some phase change cold energy storage devices with the flat miniature heat pipe arrays
Faculty of Computer and Information Sciences, Department of Information Systems and Technologies, Niğde Ömer Halisdemir University, 51240, Niğde, Türkiye
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Received:
19
March
2025
Accepted:
12
August
2025
Published online:
25
August
2025
The paper tackles the significant issue of precisely forecasting heat transfer efficacy in phase change cold energy storage systems augmented by flat microheat pipe arrays, a crucial technology for enhancing energy efficiency in industrial applications. This study integrates experimental research with artificial neural network modeling, establishing a dual methodological framework that connects experimental and computational techniques in heat control. The advanced artificial neural network models were trained using 270 experimental observations, employing the Levenberg–Marquardt method with improved multilayer perceptron architectures. The models exhibited remarkable prediction accuracy, with mean squared error values approaching zero and elevated correlation coefficients, signifying near-perfect concordance with experimental data. The artificial neural network models accurately caught these patterns with low variance, demonstrating their resilience and dependability. The artificial neural network models effectively captured the nonlinear heat transfer dynamics inherent in phase change systems, enabling high-precision predictions despite complex thermal interactions. This research enhances the current knowledge by integrating experimental findings with machine learning for Phase change material-based systems, facilitating accurate performance optimization and showcasing potential scalability to wider thermal applications. The findings highlight artificial neural network’s pivotal role in improving the efficiency of energy storage devices.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
