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

abstract

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.

keywords

ACTIVE MAGNETIC REGENERATOR; MULTIOBJECTIVE OPTIMIZATION; TEMPERATURE SPAN; PYTHON FRAMEWORK; REFRIGERATION; PERFORMANCE; MODEL

subject category

Energy & Fuels

authors

Silva, DJ; Amaral, JS; Amaral, VS

our authors

acknowledgements

The present study was developed in the scope of the Smart Green Homes Project [POCI-01-0247-FEDER-007678], a co-promotion between Bosch Termotecnologia S.A. and the University of Aveiro. It is financed by Portugal 2020 under the Competitiveness and Internationalization Operational Program, and by the European Regional Development Fund. Project CICECO-Aveiro Institute of Materials, POCI-01-0145-FEDER-007679 (FCT Ref. UID/CT/5001/2013), financed by national funds through the FCT/MEC and co-financed by FEDER under the PT2020 Partnership Agreement is acknowledged. JA acknowledge FCT IF/01089/2015 grant.

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