Temperature dependent thermal conductivity of magnetocaloric materials: Impact assessment on the performance of active magnetic regenerative refrigerators

Silva, M. R. da; Davarpanah, Amin; Amaral, J. S.; Amaral, V. S.

doi:10.1016/j.ijrefrig.2019.06.016

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Petersen et al concluded that 2D models are more adequate in leading with slow heat transfer phenomena along transverse directions (eg, for situations where heat cannot be flown completely in spheres and in the thin plates within one cycle) 99 . This criteria can be quantified using 36 :

normalΔ l < \sqrt{\frac{italickτ}{italicCρ}},

where

normalΔ l

is the transverse length of the used regenerator geometry and

τ

is the period of the fluid flow. If this criteria is verified, a 1D model is sufficient to describe the caloric system.…”

Section: Thermodynamic Modelingmentioning

confidence: 99%

“…With respect to the thermal conductivities

k

, it is known that large operating frequencies demand large

k

, and small operating frequencies demand small

k

. 37 Only the model of Silva et al has used

k ((), T)

in a MC model 36 . In that investigation, it was concluded that the use of fixed

k

values leads to non‐negligible performance deviations if the thermal conductivity changes approximately 50% within a temperature window of 10 K, which may approximate actual values in some MC materials.…”

Section: Thermodynamic Modelingmentioning

confidence: 99%

“…The active magnetic regenerative cycle can be generalized for other caloric effects in the so‐called active regenerative cycle (ARC), which is nowadays used in the great majority of caloric devices. This effect 30,36 can be optimized with the so‐called utilization factor

U

U = \frac{{\dot{m}}_{f} C_{f} τ}{m_{s} < C_{s} >},

where,

m

is the mass, and the subscripts

s

and

f

stand for solid and fluid, respectively. Thus,

{\dot{m}}_{f}

is the fluid flow rate and

τ

is the fluid blow time.…”

Section: Introductionmentioning

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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normalΔ l < \sqrt{\frac{italickτ}{italicCρ}},

where

normalΔ l

is the transverse length of the used regenerator geometry and

τ

is the period of the fluid flow. If this criteria is verified, a 1D model is sufficient to describe the caloric system.…”

Section: Thermodynamic Modelingmentioning

confidence: 99%

“…With respect to the thermal conductivities

k

, it is known that large operating frequencies demand large

k

, and small operating frequencies demand small

k

. 37 Only the model of Silva et al has used

k ((), T)

in a MC model 36 . In that investigation, it was concluded that the use of fixed

k

Section: Thermodynamic Modelingmentioning

confidence: 99%

U

U = \frac{{\dot{m}}_{f} C_{f} τ}{m_{s} < C_{s} >},

where,

m

is the mass, and the subscripts

s

and

f

stand for solid and fluid, respectively. Thus,

{\dot{m}}_{f}

is the fluid flow rate and

τ

is the fluid blow time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Caloric devices: A review on numerical modeling and optimization strategies

2021

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“…Concerning thermal conductivity k, Nielsen et al (2012) demonstrated that large operating frequencies demand large k, and small operating frequencies demand small k, when simulating a complete AMR regenerator [28]. So far, Silva et al (2019) developed the only AMR numerical model that employs temperature dependent k [29]. The research group concluded that changing k approximately by 50% within a temperature window of 10 K, can significantly impact the overall AMR cycle's performance.…”

Section: Introductionmentioning

confidence: 99%

“…However, the accuracy of these models can be improved by considering temperature dependent properties. Silva et al (2019) successfully incorporated k(T) values into a magnetocaloric device model [29]. Nevertheless, in this paper, a deeper scrutiny is made on the heat transfer dynamics of a idealized MCM slab, detailing the impact of implementing more than one temperature dependent physical property.…”

Section: Introductionmentioning

confidence: 99%

Thermal Response of Magnetic Refrigerants: Combined Effect of Temperature Dependent Specific Heat and Thermal Conductivity

2022

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Device optimization plays a paramount role in current research on magnetic refrigeration. Solid state refrigerants have been characterized and numerical simulations assume a critical relevance in the development of magnetocaloric technology to have alternatives to vapour-compression systems whose operating elements have high global warming potential. Experimental studies have shown that the thermal properties of several magnetocaloric materials considerably change around their Curie temperatures (TC) and that this temperature dependency should not be dismissed. Current numerical research does not fully predict the complete thermal response of such materials, due to inaccuracies from neglecting the impact of combining both thermal conductivity (k) and specific heat (Cp) dependence on temperature. In this study, a simple unidimensional model includes k(T) and Cp(T) functions as input parameters, highlighting the relevance of considering temperature dependent thermophysical properties’ inputs when simulating the magnetic refrigerant’s heat transfer processes. The obtained results evidence that neglecting the temperature dependence of the magnetocaloric material thermophysical properties, namely its thermal conductivity and its specific heat, affects its temperature response, what may strongly affect the results after a succession of (hundreds or thousands) cycles.

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Revisiting thermal penetration depth for caloric cooling system

Qian

2020

Applied Thermal Engineering

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Temperature dependent thermal conductivity of magnetocaloric materials: Impact assessment on the performance of active magnetic regenerative refrigerators

Cited by 9 publications

References 26 publications

Caloric devices: A review on numerical modeling and optimization strategies

Caloric devices: A review on numerical modeling and optimization strategies

Thermal Response of Magnetic Refrigerants: Combined Effect of Temperature Dependent Specific Heat and Thermal Conductivity

Revisiting thermal penetration depth for caloric cooling system

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