دورية أكاديمية
Magnetocaloric effect modeling of dysprosium-transition metal based intermetallic alloys for magnetic refrigeration application using hybrid genetic algorithm based support vector regression intelligent method.
| العنوان: | Magnetocaloric effect modeling of dysprosium-transition metal based intermetallic alloys for magnetic refrigeration application using hybrid genetic algorithm based support vector regression intelligent method. |
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| المؤلفون: | Ibn Shamsah SM; Department of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia. |
| المصدر: | PloS one [PLoS One] 2024 Feb 06; Vol. 19 (2), pp. e0298431. Date of Electronic Publication: 2024 Feb 06 (Print Publication: 2024). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: San Francisco, CA : Public Library of Science |
| مواضيع طبية MeSH: | Alloys* , Dysprosium*, Refrigeration ; Physical Phenomena ; Electrons |
| مستخلص: | Intermetallic alloy containing rare earth dysprosium ions with the associated unfilled 4f shell electrons and sub-lattice of 3d-transition metal, results into fascinating magnetic properties which are useful for green refrigeration technological application. Magnetocaloric effect remains the fundamental principle upon which magnetic refrigeration technology is based while this cooling technology has advantages of cost effectiveness, high efficiency and environmental friendliness as compared with the existing conventional gas compression systems. Maximum magnetic entropy change (which controls the hugeness of magnetocaloric effect) of intermetallic alloy Dy-T-X (where T = transition metal and X = any other metal or nonmetal) is modeled in this work using hybrid genetic algorithm based support vector regression (GSVR) computational intelligent method with applied magnetic field, ionic concentration and ionic radii descriptors. The developed GSVR-G model with kernel Gaussian function outperforms GSVR-P model with polynomial function with improvement of 85.23%, 78.82% and 78.67% on the basis of the computed correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) on testing sample, respectively. The developed model further investigates the influence of applied external magnetic field on magnetocaloric effect of DyCuAl intermetallic alloy. The developed models in this work circumvent experimental challenges of magnetocaloric effect determination while the recorded precision of the developed model further opens doors for possible exploration of these intermetallic compounds for addressing environmental challenges associated with the present system of cooling. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2024 Sami M. Ibn Shamsah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| المشرفين على المادة: | 0 (Alloys) 1D4N45714Q (Dysprosium) |
| تواريخ الأحداث: | Date Created: 20240206 Date Completed: 20240208 Latest Revision: 20240210 |
| رمز التحديث: | 20240210 |
| مُعرف محوري في PubMed: | PMC10846691 |
| DOI: | 10.1371/journal.pone.0298431 |
| PMID: | 38319931 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0298431 |