A Novel Statistical Clustering Model for Predicting Thermal Conductivity of Nanofluid

Wang, Bu‐Xuan; Sheng, Wen-Yan; Peng, Xiaofeng

doi:10.1007/s10765-009-0673-4

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…The proposed model creates a non-linear relation between the effective thermal conductivity and nanoparticle volume fraction [ 72 ]. Wang et al proposed a Novel Statistical Clustering Model to determine the macroscopic characteristics of clusters, and then, the thermal conductivity of a nanofluid [ 73 ]. Sitprasert et al modified the Leong model inorder to predict both the temperature and the volume fraction dependence of the thermal conductivity of nanofluids for both non-flowing and flowing fluids [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment

2011

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The success of quenching process during industrial heat treatment mainly depends on the heat transfer characteristics of the quenching medium. In the case of quenching, the scope for redesigning the system or operational parameters for enhancing the heat transfer is very much limited and the emphasis should be on designing quench media with enhanced heat transfer characteristics. Recent studies on nanofluids have shown that these fluids offer improved wetting and heat transfer characteristics. Further water-based nanofluids are environment friendly as compared to mineral oil quench media. These potential advantages have led to the development of nanofluid-based quench media for heat treatment practices. In this article, thermo-physical properties, wetting and boiling heat transfer characteristics of nanofluids are reviewed and discussed. The unique thermal and heat transfer characteristics of nanofluids would be extremely useful for exploiting them as quench media for industrial heat treatment.

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Section: Discussionmentioning

confidence: 99%

Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment

2011

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“…For a sphere, n = 3, while for a cylinder, n = 6. The thermal conductivity of the layer and its thickness do not change with the geometry of the particle, since the layer thickness can be calculated with the Langmuir formula [32]:…”

Section: Appendix a Extension Of The Feng Modelmentioning

confidence: 99%

Heat Conduction and Microconvection in Nanofluids: Comparison between Theoretical Models and Experimental Results

et al. 2022

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A nanofluid is a suspension consisting of a uniform distribution of nanoparticles in a base fluid, generally a liquid. Nanofluid can be used as a working fluid in heat exchangers to dissipate heat in the automotive, solar, aviation, aerospace industries. There are numerous physical phenomena that affect heat conduction in nanofluids: clusters, the formation of adsorbate nanolayers, scattering of phonons at the solid–liquid interface, Brownian motion of the base fluid and thermophoresis in the nanofluids. The predominance of one physical phenomenon over another depends on various parameters, such as temperature, size and volume fraction of the nanoparticles. Therefore, it is very difficult to develop a theoretical model for estimating the effective thermal conductivity of nanofluids that considers all these phenomena and is accurate for each value of the influencing parameters. The aim of this study is to promote a way to find the conditions (temperature, volume fraction) under which certain phenomena prevail over others in order to obtain a quantitative tool for the selection of the theoretical model to be used. For this purpose, two sets (SET-I, SET-II) of experimental data were analyzed; one was obtained from the literature, and the other was obtained through experimental tests. Different theoretical models, each considering some physical phenomena and neglecting others, were used to explain the experimental results. The results of the paper show that clusters, the formation of the adsorbate nanolayer and the scattering of phonons at the solid–liquid interface are the main phenomena to be considered when φ = 1 ÷ 3%. Instead, at a temperature of 50 °C and in the volume fraction range (0.04–0.22%), microconvection prevails over other phenomena.

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“…In the mono-and hybrid-nanofluid models [43][44][45][46][47][48], Max. deviation margins are 0.47 % positive and 0.51 % negative.…”

Section: For Cuo Mn-multi-walled Cnt/cuo Hnmentioning

confidence: 99%

Thermal Conductivity Enhancement via Synthesis Produces a New Hybrid Mixture Composed of Copper Oxide and Multi-walled Carbon Nanotube Dispersed in Water: Experimental Characterization and Artificial Neural Network Modeling

et al. 2020

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Nanofluid is a solid-fluid mixture. By using one solid nanoparticle or one fluid, mono-nanofluid (MN) forms, and by using two solid nanoparticles (NPs) or two fluids, hybrid-nanofluid (HN) forms. For this study, for MN, copper oxide (CuO) and for HN, two solids, which are CuO and multi-walled carbon nanotube (MWCNT) were dispersed in base fluid which is water. After nanofluid preparation, thermal conductivity was measured, and the achievements were numerically modeled. After that, XRD-EDX were performed for the phase-structural analysis. Then, FESEM was examined for NPs-microstructural study. Thermal conductivity (TC) of MN and HN were investigated at 0.2 % to 1.0 % volume fractions (Vf) in 25 °C to 50 °C temperature (T) ranges. Thermal conductivity enhancements of 19.16 % and 37.05 % were seen at the utmost Vf and T for mono-nanofluid and hybrid-nanofluid, respectively. New correlations have been presented with R 2 = 0.9, and also Artificial Neural Network (ANN) has been done with R 2 = 0.999. For the presented correlation, 0.86 %, and 0.51 % deviations, and for the trained model, 0.41 % and 0.51 % deviations were estimated for mono-nanofluid and hybrid-nanofluid, respectively. As a final result, by adding MWCNT to CuO-H 2 O mixture, thermal conductivity is raised by 17.89 %, and the hybrid-nanofluid has acceptable heat-transfer capability.

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A Novel Statistical Clustering Model for Predicting Thermal Conductivity of Nanofluid

Cited by 17 publications

References 8 publications

Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment

Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment

Heat Conduction and Microconvection in Nanofluids: Comparison between Theoretical Models and Experimental Results

Thermal Conductivity Enhancement via Synthesis Produces a New Hybrid Mixture Composed of Copper Oxide and Multi-walled Carbon Nanotube Dispersed in Water: Experimental Characterization and Artificial Neural Network Modeling

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