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Advances in Numerical Modeling for Heat Transfer and Thermal Management: A Review of Computational Approaches and Environmental Impacts

Author

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  • Łukasz Łach

    (AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland)

  • Dmytro Svyetlichnyy

    (AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

Advances in numerical modeling are essential for heat-transfer applications in electronics cooling, renewable energy, and sustainable construction. This review explores key methods like Computational Fluid Dynamics (CFD), the Finite Element Method (FEM), the Finite Volume Method (FVM), and multiphysics modeling, alongside emerging strategies such as Adaptive Mesh Refinement (AMR), machine learning (ML), reduced-order modeling (ROM), and high-performance computing (HPC). While these techniques improve accuracy and efficiency, they also increase computational energy demands, contributing to a growing carbon footprint and sustainability concerns. Sustainable computing practices, including energy-efficient algorithms and renewable-powered data centers, offer potential solutions. Additionally, the increasing energy consumption in numerical modeling highlights the need for optimization strategies to mitigate environmental impact. Future directions point to quantum computing, adaptive models, and green computing as pathways to sustainable thermal management modeling. This study systematically reviews the latest advancements in numerical heat-transfer modeling and, for the first time, provides an in-depth exploration of the roles of computational energy optimization and green computing in thermal management. This review outlines a roadmap for efficient, environmentally responsible heat-transfer models to meet evolving demands.

Suggested Citation

  • Łukasz Łach & Dmytro Svyetlichnyy, 2025. "Advances in Numerical Modeling for Heat Transfer and Thermal Management: A Review of Computational Approaches and Environmental Impacts," Energies, MDPI, vol. 18(5), pages 1-60, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1302-:d:1606725
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    References listed on IDEAS

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    1. Xi-Xia Li & Li Wang & Chang-Yu Li & Yi-Yu Wan & Yu-Chen Qian, 2021. "Semianalytical Solution of Transient Heat Transfer for Laminated Structures under Time-Varying Boundary Conditions," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, October.
    2. Łukasz Łach & Dmytro Svyetlichnyy, 2024. "Recent Progress in Heat and Mass Transfer Modeling for Chemical Vapor Deposition Processes," Energies, MDPI, vol. 17(13), pages 1-60, July.
    3. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    4. Roger Lew & Bikash Poudel & Jaron Wallace & Tyler L. Westover, 2024. "A Reduced-Order Model of a Nuclear Power Plant with Thermal Power Dispatch," Energies, MDPI, vol. 17(17), pages 1-30, August.
    5. Daniel Chocontá Bernal & Edmundo Muñoz & Giovanni Manente & Adriano Sciacovelli & Hossein Ameli & Alejandro Gallego-Schmid, 2021. "Environmental Assessment of Latent Heat Thermal Energy Storage Technology System with Phase Change Material for Domestic Heating Applications," Sustainability, MDPI, vol. 13(20), pages 1-17, October.
    6. Di Giorgio, Paolo & Di Ilio, Giovanni & Jannelli, Elio & Conte, Fiorentino Valerio, 2022. "Innovative battery thermal management system based on hydrogen storage in metal hydrides for fuel cell hybrid electric vehicles," Applied Energy, Elsevier, vol. 315(C).
    7. Jana Shafi & Mehdi Ghalambaz & Mehdi Fteiti & Muneer Ismael & Mohammad Ghalambaz, 2022. "Computational Modeling of Latent Heat Thermal Energy Storage in a Shell-Tube Unit: Using Neural Networks and Anisotropic Metal Foam," Mathematics, MDPI, vol. 10(24), pages 1-26, December.
    8. Teresa Murino & Roberto Monaco & Per Sieverts Nielsen & Xiufeng Liu & Gianluigi Esposito & Carlo Scognamiglio, 2023. "Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations," Energies, MDPI, vol. 16(15), pages 1-14, August.
    9. Jiang, Mengting & Speetjens, Michel & Rindt, Camilo & Smeulders, David, 2023. "A data-based reduced-order model for dynamic simulation and control of district-heating networks," Applied Energy, Elsevier, vol. 340(C).
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