Numerical studies on the influence of natural convection under inclination on optimal aluminium proportions and fin spacings in a rectangular aluminium finned latent-heat thermal energy storage

Kasper, Lukas; Pernsteiner, Dominik; Koller, Martin; Schirrer, Alexander; Jakubek, Stefan; Hofmann, René

doi:10.1016/j.applthermaleng.2020.116448

Cited by 24 publications

(4 citation statements)

References 75 publications

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“…The governing equations for PCM are given as follows: Table 1. Thermophysical properties of RT25 (Biwole et al, 2013;Faghani et al, 2018), plexiglass (Kabbara et al, 2016) and aluminium (Kasper et al, 2021) Material…”

Section: Governing Equationsmentioning

confidence: 99%

A numerical study on the effects of inclination angle and container material on thermal energy storage by phase change material in a thick-walled disc

Kiyak,

Oztop,

Aksoy

2024

HFF

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Purpose The purpose of this study is to examine the effects of inclination angle on the thermal energy storage capability of a phase change material (PCM) within a disc-shaped container. Different container materials are also tested such as plexiglass and aluminium. This study aims to assess the energy storage capacity, melting behaviour and temperature distributions of PCM with a specific melting range (22°C–26°C) for various governing parameters such as inclination angles, aspect ratios (AR) and temperature differences (ΔT) and compare the melting behaviour and energy storage performance of PCM in aluminium containers to those in plexiglass containers. Design/methodology/approach A finite volume approach was adopted to evaluate the thermal energy storage capability of PCMs. Five inclination angles ranging from 0° to 180° were considered and the energy storage capacity. Also, the melting behaviour of the PCM and temperature distributions of the container with different materials were tested. Two different AR and ΔT values were chosen as parameters to analyse for their effects on the melting performance of the PCM. Conjugate heat transfer problem is solved to see the effects of conduction mode of heat transfer. Findings The results of the study indicate that as AR decreases, the effect of the inclination angles on the energy storage capacity of the PCM decreases. For lower ΔT, the difference between the maximum and minimum stored energies was 20.88% for AR = 0.20, whereas it was 6.85% for AR = 0.15. Furthermore, under the same conditions, the PCM stored 8.02% more energy in plexiglass containers than in aluminium containers. Originality/value This study contributes to the understanding of the influence of inclination angle, container material, AR and ΔT on the thermal energy storage capabilities of PCM in a novel designed container. The findings highlight the importance of AR in mitigating the effect of the inclination angle on energy storage capacity. Additionally, comparing aluminium and plexiglass containers provides insights into the effect of container material on the melting behaviour and energy storage properties of PCM.

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Section: Governing Equationsmentioning

confidence: 99%

A numerical study on the effects of inclination angle and container material on thermal energy storage by phase change material in a thick-walled disc

Kiyak,

Oztop,

Aksoy

2024

HFF

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“…Enhancing the thermal conductivity of any device can be achieved by increasing the surface area, also known as a fin. Small, low-cost fins can be connected to increase the rate of thermal conductivity, which has attracted the attention of numerous researchers (Adeyeye et al ., 2020; Gireesha et al ., 2020; Kasper et al ., 2020; Choudhari et al ., 2020). Aziz and Khani (2010) evaluated the performance of a radial porous fin using the Homotopy Analysis method and compared the analytical results with numerical computation.…”

Section: Introductionmentioning

confidence: 99%

Supervised machine learning techniques for optimization of heat transfer rate of Cu-H₂O nanofluid flow over a radial porous fin

Raza,

Mustaq

et al. 2023

MMMS

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PurposeThe purpose of this paper is to study the thermal behavior of radial porous fin surrounded by water-base copper nanoparticles under the influence of radiation.Design/methodology/approachIn order to optimize the response variable, the authors perform sensitivity analysis with the aid of response surface methodology (RSM). Moreover, this study enlightens the applications of artificial neural networks (ANN) for predicting the temperature gradient. The governing modeled equations are firstly non-dimensionalized and then solved with the aid of Runge–Kutta fourth order together with the shooting method in order to guess the initial conditions.FindingsNumerical results are analyzed and presented in the form of tables and graphs. This study reveals that the temperature of the fin is decreasing as the wet porous parameter increases (m2) and the temperature for 10% concentration of nanoparticles are higher than 5 and 1%. Physical parameters involved in the study are analyzed and processed through RSM. It is come to know that sensitivity of temperature gradient to radiative parameter (Nr) and convective parameter (Nc) is positive and negative to dimensionless ambient temperature (θa). Furthermore, after ANN training it can be argued that the established model can efficiently be used to predict the temperature gradient over a radial porous fin for the copper-water nanofluid flow.Originality/valueTo the best of our knowledge, only a few attempts have been made to analyze the thermal behavior of radial porous fin surrounded by copper-based nanofluid under the influence of radiation and convection.

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“…Natural convection in enclosures has been studied extensively due to its applications in different industries such as solar energy [1][2][3], energy storage [4,5], nuclear reactor [6], chemical and roll-coating [7,8], to name a few. One of the main challenges those industries face is to maintain high thermal efficiency in their respective applications without losing the thermal conductivity of existing models.…”

Section: Introductionmentioning

confidence: 99%

Lattice Boltzmann simulation of natural convection of ethylene glycol-alumina nanofluid in a C-shaped enclosure with MFD viscosity through a parallel computing platform and quantitative parametric assessment

et al. 2023

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The multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) was considered in this study to numerically analyse the effects of magnetic field dependent (MFD) viscosity on the natural convection of ethylene glycol (C$_2$H$_6$O$_2$)-alumina (Al$_2$O$_3$) nanofluid in a side heated two-dimensional C-shaped enclosure using graphics processing unit (GPU) by a computing unified device architecture (CUDA) C parallel computing platform. Numerical simulations were performed at multifarious Rayleigh numbers, Hartmann numbers, and the different magnetic field inclination angles to study the heat transfer and various flow patterns under magnetic field-dependent (MFD) viscosity. The numerical solutions were presented by varying volume fraction of nanoparticles, Rayleigh numbers, viscous parameters, magnetic inclination angles, and Hartman numbers on streamlines, isotherm, local and average Nusselt number and temperature. Further correlation developments were conducted through Levenberg-Marquardt data-driven algorithm to investigate the influence of all the parameters on average Nusselt numbers, entropy generation, and fluid irreversibility parameter. The findings demonstrated that as the Rayleigh numbers augmented, the average Nusselt number increased significantly due to the influence of buoyancy, whereas under the influence of Hartmann numbers, average Nusselt numbers decreased due to the dominance of magnetic field strength and Lorentz force. However, the heat transfer continued to improve if the concentration of the nanoparticles increased, thus showcasing the importance of hybrid nanofluid. In addition, the entropy generation impact across the cavity for the ethylene glycol-alumina nanofluid was greatly enhanced by a stronger buoyancy influence. The findings from this study provide major evidence that the inclusion of nanofluid can be a great alternative fluid to improve the heat transfer efficiency under the influence of magnetic field strength, which can be implemented and further investigated in electronic cooling system and chip designing industry which extensively work with C-shaped equipment.

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Numerical studies on the influence of natural convection under inclination on optimal aluminium proportions and fin spacings in a rectangular aluminium finned latent-heat thermal energy storage

Cited by 24 publications

References 75 publications

A numerical study on the effects of inclination angle and container material on thermal energy storage by phase change material in a thick-walled disc

A numerical study on the effects of inclination angle and container material on thermal energy storage by phase change material in a thick-walled disc

Supervised machine learning techniques for optimization of heat transfer rate of Cu-H₂O nanofluid flow over a radial porous fin

Lattice Boltzmann simulation of natural convection of ethylene glycol-alumina nanofluid in a C-shaped enclosure with MFD viscosity through a parallel computing platform and quantitative parametric assessment

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Numerical studies on the influence of natural convection under inclination on optimal aluminium proportions and fin spacings in a rectangular aluminium finned latent-heat thermal energy storage

Cited by 24 publications

References 75 publications

A numerical study on the effects of inclination angle and container material on thermal energy storage by phase change material in a thick-walled disc

A numerical study on the effects of inclination angle and container material on thermal energy storage by phase change material in a thick-walled disc

Supervised machine learning techniques for optimization of heat transfer rate of Cu-H2O nanofluid flow over a radial porous fin

Lattice Boltzmann simulation of natural convection of ethylene glycol-alumina nanofluid in a C-shaped enclosure with MFD viscosity through a parallel computing platform and quantitative parametric assessment

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Supervised machine learning techniques for optimization of heat transfer rate of Cu-H₂O nanofluid flow over a radial porous fin