-
A numerical modelling of a multi-layer LaFeCoSi active magnetic regenerator by using artificial neural networksMaiorino, Angelo ...One of the main problems in the framework of magnetic refrigeration regards low adiabatic temperature changes that occur in the magnetocaloric materials, which limits the widespread application of ... this technology. Therefore, the major effort of researchers is focused on the development of multi-layer Active Magnetic Regenerators, which allows to enlarge the temperature span of a magnetic refrigerator. The use of numerical models can help to understand the feasibility of such application with less effort in comparison with the use of experimental facilities. One of the main challenges in designing a numerical model of a multi-layer Active Magnetic Regenerator is the effective incorporation of the magnetocaloric data of different magnetocaloric materials, which are fundamental to correctly optimize the configuration of such a device with the aim to improve its performance. These data are usually obtained experimentally from different measurements and their integration into the numerical model is challenging. Therefore, this work proposes a modified multi-layer Active Magnetic Regenerator numerical model based on Artificial Neural Networks to integrate the magnetocaloric properties of magnetocaloric materials, allowing an easier and a more reliable implementation of the real properties of magnetocaloric materials. The proposed model was tested simulating a four-layer and a seven-layer LaFeCoSi Active Magnetic Regenerator. The use of Artificial Neural Networks to integrate the magnetocaloric properties of magnetocaloric materials into the multi-layer Active Magnetic Regenerator allowed to improve the accuracy of the model in comparison with the commonly used technique (i.e., Curie temperature shift method) when compared to the experimental data. Indeed, the maximum error of the maximum temperature span with zero thermal load was reduced from about 13 K to 6.6 K, for the seven-layer configuration, and from about 4.1 K to 1.0 K, for the four-layer configuration. Furthermore, the new model allows to obtain more reliable simulated data about the effectiveness of each layer of the Active Magnetic Regenerator, providing a more useful tool to discuss about the optimization of its configuration. The results shows that Artificial Neural Networks can be successfully applied for integrating the magnetocaloric properties of magnetocaloric materials into a multi-layer Active Magnetic Regenerator numerical model, improving its performance. They represent an innovative way to address the problem of including magnetocaloric properties into numerical models, opening the way to other possible Machine Learning techniques as alternatives to the usual Curie temperature shifting method used in the literature to date.Source: Applied thermal engineering. - ISSN 1359-4311 (Vol. 197, Oct. 2021, str. 1-13)Type of material - article, component part ; adult, seriousPublish date - 2021Language - englishCOBISS.SI-ID - 72137475
Author
Maiorino, Angelo |
Del Duca, Manuel Gesù |
Tomc, Urban |
Tušek, Jaka |
Kitanovski, Andrej |
Aprea, Ciro
Topics
magnetic refrigeration |
magnetocaloric effect |
multi-layer regenerators |
artificial neural network |
modelling |
temperature span |
magnetno hlajenje |
magnetokalorični učinek |
večslojni regeneratorji |
modeliranje |
temperaturni razpon
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|---|
Maiorino, Angelo | ![]() |
Del Duca, Manuel Gesù | ![]() |
Tomc, Urban | 34418 |
Tušek, Jaka | 29624 |
Kitanovski, Andrej | 18580 |
Aprea, Ciro | ![]() |
Select pickup location:
Material pickup by post
Notification
Subject headings in COBISS General List of Subject Headings
Select pickup location
Pickup location | Material status | Reservation |
---|
Please wait a moment.