Analysis of hydro-thermal and entropy generation characteristics of nanofluid in an aluminium foam heat sink by employing Darcy-Forchheimer-Brinkman model coupled with multiphase Eulerian model

Ambreen, Tehmina; Saleem, Arslan; Park, Cheol Woo

doi:10.1016/j.applthermaleng.2020.115231

Cited by 53 publications

(16 citation statements)

References 60 publications

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“…Irrespective of the nanofluid type, havg increases with the augmentation of nanoparticle concentration and Re. Such enhancement in heat transfer is consistent with the literature [2,[63][64][65]. However, the rate of havg increment is highest for the single-phase and Eulerian-Eulerian models, followed by the Lagrangian-Eulerian model.…”

Section: Validation Of the Homogenous Modelsupporting

confidence: 92%

“…Nanofluids are nanotechnology-based novel fluids of ultrahigh thermal efficiency. Over the past several decades, these thermal fluids have gained overwhelming attention in relevance to their potential applications in microelectronics, transportation, refrigeration, solar, nuclear and space technologies [1,2]. Even at a low concentration, nanoparticles possess high effective surface area and therefore exhibit exceptional thermal efficiency, flexible thermophysical properties, suspension stability and enlarged solid-particle interface for maximum interphase heat exchange.…”

Section: Introductionmentioning

confidence: 99%

“…Several comparative reports also emphasised the accuracy of this approach [24][25][26][27][28]. However, a group of researchers condemned homogenous approximation of highly concentrated nanofluids due to the significant influence of interphase interactive forces that are neglected in this approach [2,[29][30][31][32][33][34][35][36][37]. Analysing forced convection of Al2O3/H2O, TiO2/H2O and Cu/H2O nanofluids, Nishat et al [38] demonstrated that the homogeneous model accurately predicts the highly concentrated nanofluids (0.5% ≤ φ ≤ 2%) compared with the Lagrangian-Eulerian model.…”

Section: Introductionmentioning

confidence: 99%

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Homogeneous and Multiphase Analysis of Nanofluids Containing Nonspherical MWCNT and GNP Nanoparticles Considering the Influence of Interfacial Layering

2021

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The practical implication of nanofluids is essentially dependent on their accurate modelling, particularly in comparison with the high cost of experimental investigations, yet the accuracy of different computational approaches to simulate nanofluids remains controversial to this day. Therefore, the present study is aimed at analysing the homogenous, multiphase Eulerian–Eulerian (volume of fluid, mixture, Eulerian) and Lagrangian–Eulerian approximation of nanofluids containing nonspherical nanoparticles. The heat transfer and pressure drop characteristics of the multiwalled carbon nanotubes (MWCNT)-based and multiwalled carbon nanotubes/graphene nanoplatelets (MWCNT/GNP)-based nanofluids are computed by incorporating the influence of several physical mechanisms, including interfacial nanolayering. The accuracy of tested computational approaches is evaluated by considering particle concentration and Reynolds number ranges of 0.075–0.25 wt% and 200–470, respectively. The results demonstrate that for all nanofluid combinations and operational conditions, the Lagrangian–Eulerian approximation provides the most accurate convective heat transfer coefficient values with a maximum deviation of 5.34% for 0.25 wt% of MWCNT–water nanofluid at the largest Reynolds number, while single-phase and Eulerian–Eulerian multiphase models accurately estimate the thermal fields of the diluted nanofluids at low Reynolds numbers, but overestimate the results for denser nanofluids at high Reynolds numbers.

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Section: Validation Of the Homogenous Modelsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Homogeneous and Multiphase Analysis of Nanofluids Containing Nonspherical MWCNT and GNP Nanoparticles Considering the Influence of Interfacial Layering

2021

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“…The simulation data were compared with the experimental data, verifying that the CFD calculation results were in good agreement with the experimental results. Azarafza et al (2020) used the CFD method to simulate the filtration process of an engine. The error between the simulation results and the experimental results was less than 15%.…”

Section: Introductionmentioning

confidence: 99%

Optimization of structure parameters in a coal pyrolysis filtration system based on CFD and quadratic regression orthogonal combination and a genetic algorithm

Liu

Zhao

Liu

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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An optimization method of structure parameters based on the quadratic regression orthogonal combination (QROC) and Genetic Algorithm (GA) is proposed in this work. The following work has been conducted to improve the performance of the coal pyrolysis filtration system and prolong the service life of the filter tubes based on QROC-GA method. Firstly, a simulation model is established and two factors always are chosen as optimization objectives. Then one single factor regression prediction algorithm is used to optimize each factor separately while the result was not satisfactory. Secondly, QROC is introduced to achieve the optimization of two factors in the filtration system. The regression relationship is obtained proved to be effective by statistical test and back propagation neural network (BPNN). Finally, a QROC-GA method is established to find the optimization points. Then a verification calculation is done with CFD again. The optimal result has the parameters that ϕ = 40°a nd ψ = 25°. From the simulation results, the mean square error is 0.401. The mean square error is 0.4992 by the QROC-GA results. The errors are within 0.1 between CFD and QROC-GA. The QROC-GA model has a good effect in the prediction of models under changing parameters, and the significance is also confirmed.

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“…Entropy generation shows increasing trend for larger Brinkman number. Attributes of entropy production in Newtonian fluid with Darcy model was analyzed by Ambreen et al [27]. Cho [28] takes a square cavity whose some walls are heated and filled it by Cu-water nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Influence of Heat Generation/Absorption and Stagnation point on Polystyrene -TiO2/H 2 O Hybrid Nanofluid

Masood

Farooq

2021

Preprint

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This article focuses on hybrid nanofluid flow induced by stretched surface. The present context covers stagnation point flow of a hybrid nanofluid with the effect of heat gen-eration/absorption. Currently most famous class of nanofluids is Hybrid nanofluid. It contains polystyrene and titanium oxide as a nanoparticles and water as a base fluid. First time attributes of heat transfer are evaluated by utilizing polystyrene-TiO 2 / H 2 O hybrid nanofluid with heat gen-eration/absorption. Partial differential equations are converted into ordinary differential equation by using appropriate transformations for heat and velocity. Homotopy analysis method is operated for solution of ordinary differential equations. Flow and heat are disclosed graphically for unlike parameters. Resistive force and heat transfer rate is deliberated mathematically and graphically. It is deduced that velocity field enhanced for velocity ratio parameter whereas temperature field decays for heat generation/absorption coefficient. To judge the production of any engineering system entropy generation is also calculated. It is noticed that entropy generation grows for Prandtl number and Eckert number while it shows opposite behaviour for temperature difference parameter.

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Analysis of hydro-thermal and entropy generation characteristics of nanofluid in an aluminium foam heat sink by employing Darcy-Forchheimer-Brinkman model coupled with multiphase Eulerian model

Cited by 53 publications

References 60 publications

Homogeneous and Multiphase Analysis of Nanofluids Containing Nonspherical MWCNT and GNP Nanoparticles Considering the Influence of Interfacial Layering

Homogeneous and Multiphase Analysis of Nanofluids Containing Nonspherical MWCNT and GNP Nanoparticles Considering the Influence of Interfacial Layering

Optimization of structure parameters in a coal pyrolysis filtration system based on CFD and quadratic regression orthogonal combination and a genetic algorithm

Influence of Heat Generation/Absorption and Stagnation point on Polystyrene -TiO2/H 2 O Hybrid Nanofluid

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