Experimental investigation of turbulent flow convection heat transfer of MgO/water nanofluid at low concentrations – Prediction of aggregation effect of nanoparticles

Motevasel, Mohsen

doi:10.18280/ijht.350409

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…In the same way, Motevasel et al [3] investigate experimentally at low concentration the effect of MgOnanoparticles aggregation on the nanofluid's thermal conductivity and viscosity and a relatively good agreements was found between the proposed fractal models and the experimental values.…”

Section: Introductionmentioning

confidence: 71%

Transition to Chaotic Natural Convection of Cu-water Nanofluid in an Inclined Square Enclosure

Boudjeniba¹,

Laouer²,

Laouar³

et al. 2019

IJHT

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In this paper, the scenario leading to chaos in natural convection of Cu-water nanofluid inside an inclined square enclosure with the aspect ratio equal to unity is numerically investigated. The enclosure is heated from one part of the side and cooled through two other opposite half sides. The governing equations and the corresponding boundary conditions are solved numerically using the finite difference method. The effect of Rayleigh number and the volume fraction on natural convection flow are analyzed. The obtained results indicate that the mode of fluid flow which is initially stationary, passes by a periodic mode across a supercritical Hopf bifurcation, then quasi periodic at two incommensurable frequencies, before reaching the final stage of chaotic convection for both pure fluid and nanofluid. The sequences of bifurcation are presented graphically; it was found that the presence of suspended nanoparticles inside the base fluid causes significantly the delay of this transition.

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Section: Introductionmentioning

confidence: 71%

Transition to Chaotic Natural Convection of Cu-water Nanofluid in an Inclined Square Enclosure

Boudjeniba¹,

Laouer²,

Laouar³

et al. 2019

IJHT

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“…Recently, Loong et al [30] have evaluated the heat transfer performance of various metal oxide-water nanofluids in fully developed laminar pipe flow, the evaluated metal oxide included Al 2 O 3 , CuO, MgO, TiO 2 , SiO 2 , ZnO, and ZrO 2 , and the best option is MgO-water nanofluid according to their analysis. Furthermore, the MgO-water nanofluid was found to have excellent features and seemingly can enhance convective heat transfer in low volume (<1 vol%), which is primarily studied in pipe flow cooling and other heat transfer applications, such as car radiator cooling, finned-tube heat exchanger, and corrugated mini channel heat sink [31][32][33][34][35][36]. Thus, this nanofluid is worth studying, but the small amount of research is focused on multiple jets impingement using this type of nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation of Flow Structure and Heat Transfer Behavior of Multiple Jet Impingement Using MgO-Water Nanofluids

et al. 2023

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Nanofluids have attracted significant attention from researchers due to their ability to significantly enhance heat transfer, especially in jet impingement flows, which can improve their cooling performance. However, there is a lack of research on the use of nanofluids in multiple jet impingements, both in terms of experimental and numerical studies. Therefore, further investigation is necessary to fully understand the potential benefits and limitations of using nanofluids in this type of cooling system. Thus, an experimental and numerical investigation was performed to study the flow structure and heat transfer behavior of multiple jet impingement using MgO-water nanofluids with a 3 × 3 inline jet array at a nozzle-to-plate distance of 3 mm. The jet spacing was set to 3, 4.5, and 6 mm; the Reynolds number varies from 1000 to 10,000; and the particle volume fraction ranges from 0% to 0.15%. A 3D numerical analysis using ANSYS Fluent with SST k-ω turbulent model was presented. The single-phase model is adopted to predict the thermal physical nanofluid. The flow field and temperature distribution were investigated. Experimental results show that a nanofluid can provide a heat transfer enhancement at a small jet-to-jet spacing using a high particle volume fraction under a low Reynolds number; otherwise, an adverse effect on heat transfer may occur. The numerical results show that the single-phase model can predict the heat transfer trend of multiple jet impingement using nanofluids correctly but with significant deviation from experimental results because it cannot capture the effect of nanoparticles.

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“…All these strategies may be broadly classified either as Passive technique or Active technique. Passive technique includes improving properties of the fluids and materials, design modifications etc., which doesn't need any secondary power for increasing heat transfer rate whereas active technique requires auxiliary power for enhancing heat transfer [4][5][6]. Since saving energy is call for the better environment, passive techniques always gain attention first [7,8].…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

Ahmadi¹,

Ghazvini²,

Baghban³

et al. 2019

RCMA

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One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotubes (CNTs) as the result of their excellent thermal properties. In this investigation, an experiment was carried out on three kinds of CNTs-nanofluids with various CNTs added to de-ionized water to compared and analyze their thermal conductivity properties. The main purpose of this study was to introduce a combination of experimental and modelling approaches to forecast the amount of thermal conductivity using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN Methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. the regression diagram of experimental and estimated values shows an R2 coefficient of 0.9806 and 0.9579 for training and testing sections of the ANFIS method in part a, and in the b, c and d parts of the diagram, coefficients of determination were 0.9893& 0.9967 and 0.9974 & 0.9992 and 0.9996& 0.9989 for training and testing part of MLP-ANN, RBF-ANN and LSSVM models. Also, the effect of different parameters was investigated using a sensitivity analysis method which demonstrates that the temperature is the most affecting parameter on the thermal conductivity with a relevancy factor of 0.66866.

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Experimental investigation of turbulent flow convection heat transfer of MgO/water nanofluid at low concentrations – Prediction of aggregation effect of nanoparticles

Cited by 11 publications

References 25 publications

Transition to Chaotic Natural Convection of Cu-water Nanofluid in an Inclined Square Enclosure

Transition to Chaotic Natural Convection of Cu-water Nanofluid in an Inclined Square Enclosure

Experimental and Numerical Investigation of Flow Structure and Heat Transfer Behavior of Multiple Jet Impingement Using MgO-Water Nanofluids

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

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