Relative cooling power modeling of RE2TM2Y ternary intermetallic rare-earth-based magnetocaloric compounds for magnetic refrigeration application using extreme learning machine and hybrid intelligent method

IF 3.5 2区 工程技术 Q1 ENGINEERING, MECHANICAL International Journal of Refrigeration-revue Internationale Du Froid Pub Date : 2024-08-15 DOI:10.1016/j.ijrefrig.2024.08.010
Sami M. Ibn Shamsah
{"title":"Relative cooling power modeling of RE2TM2Y ternary intermetallic rare-earth-based magnetocaloric compounds for magnetic refrigeration application using extreme learning machine and hybrid intelligent method","authors":"Sami M. Ibn Shamsah","doi":"10.1016/j.ijrefrig.2024.08.010","DOIUrl":null,"url":null,"abstract":"<div><div>Ternary intermetallic rare-earth-based magnetocaloric compounds (RE<sub>2</sub>TM<sub>2</sub>Y, where RE = Gd, Tb, Dy, Ho, Er, Tm, TM= Ni, Cu, Co and <em>Y</em> = Sn, In, Cd, Ga, Al) have attracted attention lately as magnetic refrigerants in addressing major concerns of the conventional system of refrigeration. Assessment of the amount of heat transferred between cold and hot reservoirs at varying applied magnetic fields through relative cooling power (RCP) determination is costly and experimentally intensive which calls for predictive computational techniques with characteristic precision. In this contribution, intelligent-based predictive models are developed through sine activation function-based extreme learning machine (SELM) and genetically optimized support vector regression (GSVR) with Gaussian (GU) and polynomial (PY) kernel functions for data mapping and transformation using applied magnetic field and ionic radii of the constituent elements as descriptors. The GU-GSVR model exhibits superior performance compared to both the PY-GSVR and SELM models when validated using a testing set of ternary intermetallic rare-earth-based magnetocaloric compounds with improvement of 10.55% and 2.28%, respectively using correlation coefficient (CC) as assessment parameter. During model validation, the developed GU-GSVR also showcases enhanced performance across additional performance metrics, including root mean square error (RMSE) and mean absolute error (MAE). The impact of externally applied magnetic field on the RCP of different ternary intermetallic rare-earth-based magnetocaloric compounds was examined by utilizing the developed GU-GSVR model. The characteristic precision and accuracy of the developed computational intelligent models would enable adequate as well as comprehensive investigation of ternary intermetallic rare-earth-based magnetocaloric compounds for a clean system of refrigeration.</div></div>","PeriodicalId":14274,"journal":{"name":"International Journal of Refrigeration-revue Internationale Du Froid","volume":"168 ","pages":"Pages 122-134"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Refrigeration-revue Internationale Du Froid","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140700724002858","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Ternary intermetallic rare-earth-based magnetocaloric compounds (RE2TM2Y, where RE = Gd, Tb, Dy, Ho, Er, Tm, TM= Ni, Cu, Co and Y = Sn, In, Cd, Ga, Al) have attracted attention lately as magnetic refrigerants in addressing major concerns of the conventional system of refrigeration. Assessment of the amount of heat transferred between cold and hot reservoirs at varying applied magnetic fields through relative cooling power (RCP) determination is costly and experimentally intensive which calls for predictive computational techniques with characteristic precision. In this contribution, intelligent-based predictive models are developed through sine activation function-based extreme learning machine (SELM) and genetically optimized support vector regression (GSVR) with Gaussian (GU) and polynomial (PY) kernel functions for data mapping and transformation using applied magnetic field and ionic radii of the constituent elements as descriptors. The GU-GSVR model exhibits superior performance compared to both the PY-GSVR and SELM models when validated using a testing set of ternary intermetallic rare-earth-based magnetocaloric compounds with improvement of 10.55% and 2.28%, respectively using correlation coefficient (CC) as assessment parameter. During model validation, the developed GU-GSVR also showcases enhanced performance across additional performance metrics, including root mean square error (RMSE) and mean absolute error (MAE). The impact of externally applied magnetic field on the RCP of different ternary intermetallic rare-earth-based magnetocaloric compounds was examined by utilizing the developed GU-GSVR model. The characteristic precision and accuracy of the developed computational intelligent models would enable adequate as well as comprehensive investigation of ternary intermetallic rare-earth-based magnetocaloric compounds for a clean system of refrigeration.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用极端学习机和混合智能方法为 RE2TM2Y 三元金属间稀土基磁致化合物建立磁制冷应用的相对制冷功率模型
三元金属间稀土基磁致冷化合物(RE2TM2Y,其中 RE = Gd、Tb、Dy、Ho、Er、Tm,TM = Ni、Cu、Co,Y = Sn、In、Cd、Ga、Al)作为磁制冷剂解决了传统制冷系统的主要问题,最近引起了人们的关注。通过测定相对冷却功率 (RCP) 来评估不同外加磁场下冷热储层之间的热量传递,成本高昂且需要大量实验,因此需要具有特征精度的预测计算技术。在本文中,通过基于正弦激活函数的极端学习机(SELM)和基因优化支持向量回归(GSVR)以及高斯(GU)和多项式(PY)核函数,开发了基于智能的预测模型,使用外加磁场和组成元素的离子半径作为描述符进行数据映射和转换。与PY-GSVR 和 SELM 模型相比,GU-GSVR 模型在使用三元金属间稀土基磁致化合物测试集进行验证时表现出更优越的性能,使用相关系数 (CC) 作为评估参数,分别提高了 10.55% 和 2.28%。在模型验证过程中,所开发的 GU-GSVR 还在其他性能指标方面展示了更高的性能,包括均方根误差 (RMSE) 和平均绝对误差 (MAE)。利用所开发的 GU-GSVR 模型,研究了外部应用磁场对不同三元金属间稀土基磁致化合物 RCP 的影响。所开发的计算智能模型所具有的精度和准确性将有助于对清洁制冷系统中的三元金属间稀土基磁致化合物进行充分和全面的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
7.30
自引率
12.80%
发文量
363
审稿时长
3.7 months
期刊介绍: The International Journal of Refrigeration is published for the International Institute of Refrigeration (IIR) by Elsevier. It is essential reading for all those wishing to keep abreast of research and industrial news in refrigeration, air conditioning and associated fields. This is particularly important in these times of rapid introduction of alternative refrigerants and the emergence of new technology. The journal has published special issues on alternative refrigerants and novel topics in the field of boiling, condensation, heat pumps, food refrigeration, carbon dioxide, ammonia, hydrocarbons, magnetic refrigeration at room temperature, sorptive cooling, phase change materials and slurries, ejector technology, compressors, and solar cooling. As well as original research papers the International Journal of Refrigeration also includes review articles, papers presented at IIR conferences, short reports and letters describing preliminary results and experimental details, and letters to the Editor on recent areas of discussion and controversy. Other features include forthcoming events, conference reports and book reviews. Papers are published in either English or French with the IIR news section in both languages.
期刊最新文献
Editorial Board Data-enhanced convolutional network based on air conditioning system start/stop time prediction Start-up investigation and heat transfer enhancement analysis of a loop thermosyphon with biomimetic honeycomb-channel evaporator Optimal Intermediate Pressure Investigation in a CO₂ Transcritical Distributed Compression Refrigeration Cycle Thermodynamic and technoeconomic limitations of Brayton refrigeration for air conditioning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1