Entropy Generation Minimization Analysis of Active Magnetic Regenerators

Trevizoli, Paulo V.; Barbosa, Jader R.

doi:10.1590/0001-3765201720160427

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Note that the maximization condition can be switched to a minimization condition. For caloric device systems, the most used optimization functions

f_{i}

are the temperature span

normalΔ T

, the cooling power

P_{cool}

, the heating power

P_{heat}

, the COP, and the total cost 113,213,221 . Moreover, entropy production has also been used since its minimization results in optimized parameters 145,221 .…”

Section: Optimizationmentioning

confidence: 99%

“…For caloric device systems, the most used optimization functions

f_{i}

are the temperature span

normalΔ T

, the cooling power

P_{cool}

, the heating power

P_{heat}

, the COP, and the total cost 113,213,221 . Moreover, entropy production has also been used since its minimization results in optimized parameters 145,221 . When only magnets are being analyzed individually, the

Λ_{cool}

figure of merit has been the most used optimization variable.…”

Section: Optimizationmentioning

confidence: 99%

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Caloric devices: A review on numerical modeling and optimization strategies

2021

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Summary The discovery of giant caloric effects and further investigation on device systems that can compete with current heat management technologies in terms of efficiency have led to a major increase in modeling investigations. These models have been crucial in designing the most efficient, cheap, and robust setups with reliable performances. One example is the modeling of magnetic refrigerators and heat pumps that has been used in the optimization of critical geometrical parameters of magnetocaloric regenerators. In this paper, we review the model components of caloric heat pump and refrigerator systems, including field generation, heat transfer, caloric effects, fluid dynamics, and loss mechanisms. We also review the optimization strategies used so far, which are based on sensitive analysis, brute force approaches, statistical learning, and genetic algorithms. The analysis is applied to magnetocaloric, electrocaloric, elastocaloric, and barocaloric systems.

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“…Note that the maximization condition can be switched to a minimization condition. For caloric device systems, the most used optimization functions

f_{i}

are the temperature span

normalΔ T

, the cooling power

P_{cool}

, the heating power

P_{heat}

, the COP, and the total cost 113,213,221 . Moreover, entropy production has also been used since its minimization results in optimized parameters 145,221 .…”

Section: Optimizationmentioning

confidence: 99%

“…For caloric device systems, the most used optimization functions