Analysis and optimisation of a cascade active magnetic regenerative refrigeration system

Conference ObjectAçık Erişim

Özet

In this paper, comprehensive thermodynamic analysis and optimisation of a cascade active magnetic regenerative refrigeration system are performed. A parametric study is conducted to investigate the effects of various design parameters on the cycle performance through energy and exergy efficiencies. A multi-objective optimisation method based on a fast and elitist non-dominated sorting genetic algorithm is applied to determine the best design parameters for the system. Two objective functions utilised in the optimisation study are the total cost rate of the system and the system exergy efficiency. The total cost rate of the system is minimised while the cycle exergy efficiency is maximised using an evolutionary algorithm. To provide insight, the Pareto frontier is shown for a multi-objective optimisation. The results show that exergy efficiency could be increased by 14.53% using exergy-based optimisation and the cost could be reduced by 12% using the cost-based optimisation. © Copyright 2016 Inderscience Enterprises Ltd.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/15080Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1504/ijex.2016.075601Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorGanjehsarabi, Hadi
dc.contributor.authorDincer, Ibrahim
dc.contributor.authorGungor, Ali
dc.date.accessioned2026-10-18T20:29:52.252083810Z
dc.date.available2026-10-18T20:29:52.252083810Z
dc.date.issued2016
dc.description.abstractIn this paper, comprehensive thermodynamic analysis and optimisation of a cascade active magnetic regenerative refrigeration system are performed. A parametric study is conducted to investigate the effects of various design parameters on the cycle performance through energy and exergy efficiencies. A multi-objective optimisation method based on a fast and elitist non-dominated sorting genetic algorithm is applied to determine the best design parameters for the system. Two objective functions utilised in the optimisation study are the total cost rate of the system and the system exergy efficiency. The total cost rate of the system is minimised while the cycle exergy efficiency is maximised using an evolutionary algorithm. To provide insight, the Pareto frontier is shown for a multi-objective optimisation. The results show that exergy efficiency could be increased by 14.53% using exergy-based optimisation and the cost could be reduced by 12% using the cost-based optimisation. © Copyright 2016 Inderscience Enterprises Ltd.
dc.identifier.doi10.1504/ijex.2016.075601
dc.identifier.eissn1742-8300
dc.identifier.endpage160
dc.identifier.issn1742-8297
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84964788340
dc.identifier.startpage143
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/15080
dc.identifier.volume19
dc.identifier.wosWOS:000377353200002
dc.language.isoen
dc.publisherInderscience Publishers
dc.relation.ispartofInternational Journal of Exergy
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAMR Cycle
dc.subjectExergy Efficiency
dc.subjectGenetic Algorithm Magnetic Refrigeration
dc.subjectMulti-Objective Optimisation
dc.subjectExergy
dc.subjectGenetic algorithms
dc.subjectMagnetic refrigeration
dc.subjectMagnetism
dc.subjectRefrigeration
dc.subjectThermoanalysis
dc.subjectActive magnetic regenerative refrigerations
dc.subjectDesign parameters
dc.subjectElitist non-dominated sorting genetic algorithms
dc.subjectEnergy and exergy efficiency
dc.subjectExergy efficiencies
dc.subjectObjective functions
dc.subjectThermo dynamic analysis
dc.subjectMultiobjective optimization
dc.titleAnalysis and optimisation of a cascade active magnetic regenerative refrigeration system
dc.typeConference Object
dspace.entity.typePublication
rdl.citation.scopus11
rdl.citation.wos10
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)
rdl.wos.indexConference Proceedings Citation Index - Science (CPCI-S)