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.
Highlights
One of the goals of the 2030 United Nations agenda for sustainable development is to “ensure access to affordable, reliable, sustainable and modern energy for all.” One way for energy consumption to become more sustainable is to increase the energy efficiency of common household devices, such as water heaters
In this work we present a new method for dimensioning magnetocaloric systems
For the example shown above, the computation of simulations was performed for 8 values of ν ranging from 0.625 to 5 Hz, 7 of ǫ ranging from 0.357 to 2.5 mm, 7 lp ranging from 0.357 to 2.5 mm, 7 lMCM ranging from 1.423 to 10 cm, 7 lHEX ranging from 1.423 to 10 cm, 7 d ranging from 1.423 to 10 cm, and 7 s ranging from 1.423 to 10 cm, i.e., a total of 941,192 different combinations
Summary
One of the goals of the 2030 United Nations agenda for sustainable development is to “ensure access to affordable, reliable, sustainable and modern energy for all.” One way for energy consumption to become more sustainable is to increase the energy efficiency of common household devices, such as water heaters. One important breakthrough that increased the research on the development of new magnetocaloric systems was the use of the active magnetic regenerative cycle, where the magnetocaloric material acts as both the refrigerant and regenerator (Barclay and Steyert, 1982). Roy et al (2017) used a genetic algorithm in their multiobjective optimization model These parametric investigations achieved the goal of dimensioning magnetocaloric systems, the development of a complete and reliable systematic dimensioning technique has been hindered by the computational cost of performing brute force computation, i.e., of systematically performing the numerical computation of systems with a large number of combinations of parameters to be dimensioned. The performance of the method is showed for the temperature span, heating power and COP
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