Performance evaluation of an active magnetic regenerator for cooling applications – part II: Mathematical modeling and thermal losses

Trevizoli, Paulo V.; Nakashima, Alan T.D.; Barbosa, Jader R.

doi:10.1016/j.ijrefrig.2016.07.010

Cited by 50 publications

(36 citation statements)

References 25 publications

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“…Figure 3 displays the evolution of the generated entropy as a function of the nanoparticle concentration. The different contributions to Equation (12) have been integrated both along the regenerator and over a cycle after the system reached a steady-state regime. For φ = 0%, convection S conv contributes to 87.71% of the total generated entropy S gen .…”

Section: Influence Of the Nanoparticle Concentrationmentioning

confidence: 99%

“…Numerical 1D or 2D models appeared also as valuable tools to design new active magnetic regenerative refrigeration (AMRR) systems [8][9][10][11][12][13][14][15][16]. Tagliafico et al [8] considered a reciprocating AMR with powder of gadolinium and investigated the influences of both the utilization factor UF (within the range [0.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Trevizoli et al [12] quantified the internal (axial conduction, demagnetization, viscous losses) and external (heat transfer through the casing, dead volume, non-uniform applied magnetic field) losses in an AMR device using a 1D model. Losses to the surroundings and dead volumes are shown to have the largest impact on the AMR performance.…”

Section: Introductionmentioning

confidence: 99%

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Exergy Analysis of a Parallel-Plate Active Magnetic Regenerator with Nanofluids

Mugica

Roy

Poncet

et al. 2017

Entropy

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This paper analyzes the energetic and exergy performance of an active magnetic regenerative refrigerator using water-based Al 2 O 3 nanofluids as heat transfer fluids. A 1D numerical model has been extensively used to quantify the exergy performance of a system composed of a parallel-plate regenerator, magnetic source, pump, heat exchangers and control valves. Al 2 O 3 -water based nanofluids are tested thanks to CoolProp library, accounting for temperature-dependent properties, and appropriate correlations. The results are discussed in terms of the coefficient of performance, the exergy efficiency, and the cooling power as a function of the nanoparticle volume fraction and blowing time for a given geometrical configuration. It is shown that while the heat transfer between the fluid and solid is enhanced, it is accompanied by smaller temperature gradients within the fluid and larger pressure drops when increasing the nanoparticle concentration. It leads in all configurations to lower performance compared to the base case with pure liquid water.

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Section: Influence Of the Nanoparticle Concentrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exergy Analysis of a Parallel-Plate Active Magnetic Regenerator with Nanofluids

Mugica

Roy

Poncet

et al. 2017

Entropy

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“…gadolinium (Gd), whose physical properties, namely adiabatic temperature change, specific heat capacity and magnetization were obtained from interpolating functions generated from experimental data. The reader is referred to Trevizoli (2015) and Trevizoli et al (2016b) for a detailed presentation of the Gd physical property database. The mathematical model is described next, taking into account the following simplifying assumptions: one-dimensional, incompressible fluid flow, low-porosity porous medium (ε < 0.6) and absence of body forces (Trevizoli et al 2014b, Trevizoli andBarbosa 2015).…”

Section: -Mathematical Modelingmentioning

confidence: 99%

“…The mathematical model is described next, taking into account the following simplifying assumptions: one-dimensional, incompressible fluid flow, low-porosity porous medium (ε < 0.6) and absence of body forces (Trevizoli et al 2014b, Trevizoli andBarbosa 2015). The mathematical model and the closure relationships for packed beds of spheres used in the present analysis were compared with experimental data for passive and active magnetic regenerators (Trevizoli et al 2016a, 2016b, Trevizoli and Barbosa 2016.…”

Section: -Mathematical Modelingmentioning

confidence: 99%

Entropy Generation Minimization Analysis of Active Magnetic Regenerators

Trevizoli

Barbosa

2017

An. Acad. Bras. Ciênc.

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A performance assessment of active magnetocaloric regenerators using entropy generation minimization is presented. The model consists of the Brinkman-Forchheimer equation to describe the fluid flow and coupled energy equations for the fluid and solid phases. Entropy generation contributions due to axial heat conduction, fluid friction and interstitial heat transfer are considered. Based on the velocity and temperature profiles, local rates of entropy generation per unit volume were integrated to give the cycle-average entropy generation in the regenerator, which is the objective function of the optimization procedure. The solid matrix is a bed of gadolinium spherical particles and the working fluid is water. Performance evaluation criteria of fixed cross-section (face) area (FA) and variable geometry (VG) are incorporated into the optimization procedure to identify the most appropriate parameters and operating conditions under fixed constraints of specified temperature span and cooling capacity.

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Energy Applications of Magnetocaloric Materials

Kitanovski

2020

Advanced Energy Materials

359

156

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The need for energy‐efficient and environmentally friendly refrigeration, heat pumping, air conditioning, and thermal energy harvesting systems is currently more urgent than ever. Magnetocaloric energy conversion is among the best available alternatives for achieving these technological goals and has been the subject of substantial basic and applied research over the last two decades. The subject is strongly interdisciplinary, requiring proper understanding and efficient integration of knowledge in different specialized fields. This review article presents a historical and up‐to‐date account of the energy‐related applications of magnetocaloric materials and information about their processing and magnetic fields, thermodynamics, heat transfer, and other relevant characteristics. The article also discusses the conceptual design of magnetocaloric refrigeration and power generation systems and some guidelines for future research in the field.

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Performance evaluation of an active magnetic regenerator for cooling applications – part II: Mathematical modeling and thermal losses

Cited by 50 publications

References 25 publications

Exergy Analysis of a Parallel-Plate Active Magnetic Regenerator with Nanofluids

Exergy Analysis of a Parallel-Plate Active Magnetic Regenerator with Nanofluids

Entropy Generation Minimization Analysis of Active Magnetic Regenerators

Energy Applications of Magnetocaloric Materials

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