GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field
Packed bed thermal energy systems (PBTES) are recognized as one of the innovative technologies in the field of energy storage. This study numerically investigates the effects of a porous medium, magnetic field, mechanical vibrations, and various configurations of concrete-phase change material (PCM)...
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Elsevier
2025-05-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X2500262X |
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| author | Walid Aich Somayeh Davoodabadi Farahani Hussien Zekri Ahmed Mir Lioua Kolsi |
| author_facet | Walid Aich Somayeh Davoodabadi Farahani Hussien Zekri Ahmed Mir Lioua Kolsi |
| author_sort | Walid Aich |
| collection | DOAJ |
| description | Packed bed thermal energy systems (PBTES) are recognized as one of the innovative technologies in the field of energy storage. This study numerically investigates the effects of a porous medium, magnetic field, mechanical vibrations, and various configurations of concrete-phase change material (PCM) on the performance of PBTES. The results indicate that substituting PCM for concrete can increase the discharge-to-charge energy ratio by over 300 times. The presence of a porous medium in the PBTES system with PCM significantly enhances the charge energy ratio (by 3.81–4.14 times) compared to scenarios without a porous medium, due to its influence on the melting process of the PCM. The presence of a magnetic field, along with an increase in its intensity, positively affects the melting process and enhances charge energy, potentially increasing it by approximately 4.132–5.281 times compared to cases without a magnetic field. Mechanical vibrations also influence charge energy in the PBTES system, resulting in an improvement of 4.41–4.56 times compared to the no-vibration scenario, with optimal efficiency achieved at A = 1e-5 m and f = 0.1 Hz. Notably, the use of a porous medium, magnetic field, and forced vibrations reduces discharge energy by approximately 0.34–0.37, 0.36 to 0.47, and 0.39 to 0.41 times, respectively, compared to the baseline scenario. Utilizing the Group Method of Data Handling (GMDH) neural network model based on the available data in this study, the discharge energy to charge energy ratio has been estimated, and the model has accurately predicted the desired parameter with a high degree of precision. |
| format | Article |
| id | doaj-art-d816cfefd6054a76ae69d064994921c9 |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-d816cfefd6054a76ae69d064994921c92025-08-20T02:17:28ZengElsevierCase Studies in Thermal Engineering2214-157X2025-05-016910600210.1016/j.csite.2025.106002GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic fieldWalid Aich0Somayeh Davoodabadi Farahani1Hussien Zekri2Ahmed Mir3Lioua Kolsi4Department of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi ArabiaSchool of Mechanical Engineering, Arak University of Technology, 38181-41167, Arak, Iran; Corresponding author.Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho, Kurdistan Region, IraqDepartment of Chemical and Materials Engineering, College of Engineering, Northern Border University, P.O. Box 1321, Arar, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi ArabiaPacked bed thermal energy systems (PBTES) are recognized as one of the innovative technologies in the field of energy storage. This study numerically investigates the effects of a porous medium, magnetic field, mechanical vibrations, and various configurations of concrete-phase change material (PCM) on the performance of PBTES. The results indicate that substituting PCM for concrete can increase the discharge-to-charge energy ratio by over 300 times. The presence of a porous medium in the PBTES system with PCM significantly enhances the charge energy ratio (by 3.81–4.14 times) compared to scenarios without a porous medium, due to its influence on the melting process of the PCM. The presence of a magnetic field, along with an increase in its intensity, positively affects the melting process and enhances charge energy, potentially increasing it by approximately 4.132–5.281 times compared to cases without a magnetic field. Mechanical vibrations also influence charge energy in the PBTES system, resulting in an improvement of 4.41–4.56 times compared to the no-vibration scenario, with optimal efficiency achieved at A = 1e-5 m and f = 0.1 Hz. Notably, the use of a porous medium, magnetic field, and forced vibrations reduces discharge energy by approximately 0.34–0.37, 0.36 to 0.47, and 0.39 to 0.41 times, respectively, compared to the baseline scenario. Utilizing the Group Method of Data Handling (GMDH) neural network model based on the available data in this study, the discharge energy to charge energy ratio has been estimated, and the model has accurately predicted the desired parameter with a high degree of precision.http://www.sciencedirect.com/science/article/pii/S2214157X2500262XPacked bed thermal energy systemMechanical vibrationsMagnetic fieldPorous mediumPCMGMDH |
| spellingShingle | Walid Aich Somayeh Davoodabadi Farahani Hussien Zekri Ahmed Mir Lioua Kolsi GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field Case Studies in Thermal Engineering Packed bed thermal energy system Mechanical vibrations Magnetic field Porous medium PCM GMDH |
| title | GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field |
| title_full | GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field |
| title_fullStr | GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field |
| title_full_unstemmed | GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field |
| title_short | GMDH-neural network approaches for packed bed thermal energy system based on PCM integrated with a porous medium and magnetic field |
| title_sort | gmdh neural network approaches for packed bed thermal energy system based on pcm integrated with a porous medium and magnetic field |
| topic | Packed bed thermal energy system Mechanical vibrations Magnetic field Porous medium PCM GMDH |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X2500262X |
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