ANNMagnetocaloricProperties

Version 1.0.2 (658 KB) by Angelo Maiorino

An application of ANNs to evaluate thermodynamic properties of magnetocaloric materials

https://github.com/LTFUNISA/ANNMagnetocaloricProperties

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Updated 23 Jul 2019

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Here, we present a new procedure to evaluate thermodynamic properties of magnetocaloric materials based on Artificial Neural Networks (ANNs). This methodology has been developed to speed up the characterisation of new magnetocaloric materials and facilitate the design process for a magnetic refrigerator. Indeed, it requires a small amount of experimental data to characterise an MCM accurately, in comparison with other commonly-used techniques.
Three different codes are provided to run the procedure. The first code is related to the training of the ANN starting from the experimental data. The second code allows to use the evaluation method within an AMR numerical simulation model. The last code provides a database of the thermodynamic properties of magnetocaloric materials, based on the learning capabilities of the Artificial Neural Networks.
Please check the README file for further information.

Cite As

Maiorino, Angelo, et al. “Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks.” Energies, vol. 12, no. 10, MDPI AG, May 2019, p. 1871, doi:10.3390/en12101871.

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Code

Versions that use the GitHub default branch cannot be downloaded

Version	Published	Release Notes
1.0.2	23 Jul 2019	Link to GitHub Repository
1.0.1	12 Jun 2019	Image uploaded	Download
1.0.0	12 Jun 2019		Download

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MLA	Maiorino, Angelo, et al. “Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks.” Energies, vol. 12, no. 10, MDPI AG, May 2019, p. 1871, doi:10.3390/en12101871.
APA	Maiorino, A., Duca, M. G. D., Tušek, J., Tomc, U., Kitanovski, A., & Aprea, C. (2019). Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks. Energies, 12(10), 1871. MDPI AG. Retrieved from https://doi.org/10.3390%2Fen12101871
BibTeX	@article{Maiorino_2019, doi = {10.3390/en12101871}, url = {https://doi.org/10.3390%2Fen12101871}, year = 2019, month = {may}, publisher = {{MDPI} {AG}}, volume = {12}, number = {10}, pages = {1871}, author = {Angelo Maiorino and Manuel Ges{\`{u}} Del Duca and Jaka Tu{\v{s}}ek and Urban Tomc and Andrej Kitanovski and Ciro Aprea}, title = {Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks}, journal = {Energies} }