ǂa ǂnovel approach based on indirect measurements using artificial neural networks
Angelo Maiorino (Avtor), Manuel Gesù Del Duca (Avtor), Jaka Tušek (Avtor), Urban Tomc (Avtor), Andrej Kitanovski (Avtor), Ciro Aprea (Avtor)

Povzetek

The thermodynamic characterisation of magnetocaloric materials is an essential task when evaluating the performance of a cooling process based on the magnetocaloric effect and its application in a magnetic refrigeration cycle. Several methods for the characterisation of magnetocaloric materials and their thermodynamic properties are available in the literature. These can be generally divided into theoretical and experimental methods. The experimental methods can be further divided into direct and indirect methods. In this paper, a new procedure based on an artificial neural network to predict the thermodynamic properties of magnetocaloric materials is reported. The results show that the procedure provides highly accurate predictions of both the isothermal entropy and the adiabatic temperature change for two different groups of magnetocaloric materials that were used to validate the procedure. In comparison with the commonly used techniques, such as the mean field theory or the interpolation of experimental data, this procedure provides highly accurate, time-effective predictions with the input of a small amount of experimental data. Furthermore, this procedure opens up the possibility to speed up the characterisation of new magnetocaloric materials by reducing the time required for experiments.

Ključne besede

magnetno hlajenje;magnetokalorični učinek;gadolinij;nevronske mreže;magnetic refrigeration;magnetocaloric effect;gadolinium;artificial neural network;modelling;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 536:697(045)
COBISS: 16618011 Povezava se bo odprla v novem oknu
ISSN: 1996-1073
Št. ogledov: 278
Št. prenosov: 74
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: magnetno hlajenje;magnetokalorični učinek;gadolinij;nevronske mreže;
Vrsta dela (COBISS): Članek v reviji
Strani: f. 1-22
Letnik: ǂVol. ǂ12
Zvezek: ǂiss. ǂ10
Čas izdaje: May 2019
DOI: 10.3390/en12101871
ID: 12855951
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