Broad Multi-Parameter Dimensioning of Magnetocaloric Systems Using Statistical Learning Classifiers

• Published in: Frontiers in energy research Vol. 8
• Main Authors: Silva, Daniel J., Amaral, João S., Amaral, Vitor S.
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 23.06.2020
• Subjects: caloric materials; dimensioning algorithm; machine learning; magnetocaloric; statistical method
• ISSN: 2296-598X; 2296-598X
• DOI: 10.3389/fenrg.2020.00121

Prototyping innovative energy devices is a complex multivariable dimensioning problem. For the case of magnetocaloric systems, one aims to obtain an optimized balance between energy conversion performance, useful power generated, and power consumed. In these devices, modeling is entering a mature phase, but dimensioning is still time consuming. We have developed a technique that dimensions any type of magnetocaloric system by training statistical learning classifiers that are used to simulate the computation of a very large number of systems with different combinations of parameters to be dimensioned. We used this method in the dimensioning of a magnetocaloric heat pump aiming at optimizing the temperature span, heating power, and coefficient of performance, obtaining an f-score of 95%. The respective classifier was used to mimic over 940 thousand computed systems. The gain in computation time was 300 times that of computing numerically the system for each combination of parameters.