Computational Methods for Energy Storage

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 527

Special Issue Editors


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Guest Editor
Faculty of Engineering, Department of Mechanical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
Interests: microchannels heat exchangers; air conditioning systems–thermal storage systems; nanofluids and nanoparticles; snow melting systems
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Guest Editor
Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy
Interests: nonlinear dynamics; nonlinear circuits; stochastic processes; bifurcation chaos; neural networks; energy harvesting

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Guest Editor
Faculty of Engineering, Department of Mechanical Engineering King Abdulaziz University, Jeddah, Saudi Arabia
Interests: heat transfer and computational fluid dynamics; renewable energy; nanofluids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aim

This Special Issue, “Computational Methods for Energy Storage”, will be published in the open access, peer-reviewed international journal Computation. It will encompass a wide range of research areas pertaining to energy storage, including thermal, electrical, chemical, and nuclear energy. We welcome original research in various formats, including research papers, reviews, case studies, technical notes, and meeting reports. This Special Issue aims to cover various aspects of energy storage, including fundamental theory, technologies, and applications of computational methods for energy storage applications. We seek theoretical, numerical, and experimental research. Key topics include common storage mediums such as battery technologies (solid-state and lithium-ion batteries), phase change materials, thermo-chemical storage, gravity-based energy storage, and hydrogen technology storage. There is no restriction on the maximum length of papers. Full details of your research should be presented so that the results can be replicated. Numerical research should include details of the method used and comparable results in the literature.

Scope

  • The application of artificial intelligence and machine learning to energy storage technologies;
  • Battery technology;
  • Energy storage;
  • Economics, policy, environmental and global impacts;
  • Electronic energy storage devices;
  • Energy and buildings;
  • Hydrogen energy storage;
  • Nano- and micro-scale energy storage systems;
  • Natural gas storage and transportation;
  • Nuclear energy storage;
  • Numerical methods;
  • Solar energy and applications;
  • Thermal energy storage.

Dr. Abdullatif A. Gari
Dr. Michele Bonnin
Dr. Nazrul Islam Abdulhafiz
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • chemical energy storage
  • computational study of energy storage
  • electronic energy storage devices
  • energy storage
  • experimental validation
  • material and thermophysical properties
  • Newtonian and non-Newtonian fluids
  • numerical methods
  • phase change materials (PCMs)
  • solar energy

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Published Papers (1 paper)

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Research

15 pages, 3459 KB  
Article
Modeling Thermal Energy Storage Capability of Organic PCMs Confined in a 2-D Cavity
by Abdullatif A. Gari
Computation 2025, 13(9), 209; https://doi.org/10.3390/computation13090209 - 1 Sep 2025
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Abstract
Organic phase change materials (PCMs) are a useful and increasingly popular choice for thermal energy storage applications such as solar energy and building envelope thermal barriers. Buildings located in high-temperature locations are exposed to extreme weather with high solar radiation intensity. PCM envelopes [...] Read more.
Organic phase change materials (PCMs) are a useful and increasingly popular choice for thermal energy storage applications such as solar energy and building envelope thermal barriers. Buildings located in high-temperature locations are exposed to extreme weather with high solar radiation intensity. PCM envelopes could act as thermal barriers on the exterior walls to prevent excessive heat gain and save on air conditioning costs. The PCM cavity is represented as a square cavity in this project. This project studies the effect of different parameters on energy transfer through the cavity. These parameters are PCM, heat flux gain (solar radiation), and time period (day hours). One parameter was changed at a time while others remained the same. This model was simulated numerically using ANSYS FLUENT software version 6.3.26. The project was solved as a transient problem and was run for a full day in simulation time. A pressure-based model was used because it is ideal for viscous flow and suitable for mildly compressible and low-speed flow. The PISO algorithm was used here because of the transient nature of the project. Temperature and convection heat transfer flux on the inner surface were recorded to study how the inner temperature and the amount of convective heat flux gain react to different conditions after energy passes the PCM envelope. It was found that Linoleic Acid provides the highest convective heat flux gain, meaning it provides the lowest thermal resistance. On the other hand, Tricosane was found to provide the lowest convective heat flux gain, meaning it provides the highest thermal resistance. For longer days (τq < 1), the PCM was in a liquid form for a longer time, which means less conduction, while for shorter days (τq > 1), the PCM was in a solid form for a longer time. Full article
(This article belongs to the Special Issue Computational Methods for Energy Storage)
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