Estimation of Thermal Conductivity of Nanofluid Using Experimental Effective Particle Volume

Kang, Hyun Uk; Kim, Sung Hyun; Oh, Je Myung

doi:10.1080/08916150600619281

Cited by 398 publications

(172 citation statements)

References 12 publications

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“…Data for the water based nanofluids with SiO2, TiO2 and Al2O3 particles are compared in Fig. 3, 4 with experimental data [15,[34][35][36][37][38][39][40], while the dashed line corresponds to formula (1). Our data are generally in good agreement with data of other authors.…”

Section: Sio2supporting

confidence: 81%

Thermal conductivity measurements of nanofluids

Pryazhnikov

Минаков

Rudyak

et al. 2017

International Journal of Heat and Mass Transfer

212

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The paper presents the results of systematic measurements of the thermal conductivity coefficient of nanofluids at room temperature. In total, more than fifty various nanofluids based on water, ethylene glycol, and engine oil containing particles of SiO2, Al2O3, TiO2, ZrO2, CuO, and diamond were studied. The nanoparticles volume concentration ranged from 0.25 to 8% and the particles size ranged from 10 to 150 nm. It is shown that the thermal conductivity of nanofluids is not described by the classical theories (Maxwell's and so forth). The nanofluid thermal conductivity coefficient is a complicated function not only of the particle concentration, but also the particles size, their material, and type of base fluid. Measured thermal conductivity coefficients almost always exceed the values calculated by the Maxwell's formula, though nanofluids with sufficiently small particles may have thermal conductivity coefficients even lower than those predicted by the Maxwell theory. However, in all cases, the nanofluid thermal conductivity coefficient enhances with increasing particle size. It is convincingly shown that there is no direct correlation between the thermal conductivity of the nanoparticle material and the thermal conductivity of nanofluid containing these particles. The base liquid also significantly influences the effective thermal conductivity of the nanofluid. It has been confirmed that the lower the thermal conductivity of the base fluid, the higher the relative thermal conductivity coefficient of the nanofluid.

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Section: Sio2supporting

confidence: 81%

Thermal conductivity measurements of nanofluids

Pryazhnikov

Минаков

Rudyak

et al. 2017

International Journal of Heat and Mass Transfer

212

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show abstract

“…Kang et al [4] showed that the enhancement obtainable from the dispersion of ultra-dispersed diamond nanoparticles (UDD) with 30-50 nm size in EG was up to 50% at 1% volume fraction. They also showed that for 8-15 nm silver (Ag) dispersed in water at 0.4% volume fraction, there was enhancement of 10% in thermal conductivity.…”

Section: Nanofluid Is a Modified Heat Transfer Fluid Produced By Homomentioning

confidence: 99%

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

Adio

Mehrabi

Sharifpur

et al. 2016

International Communications in Heat and Mass Transfer

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Desirable effective viscosity behaviour is an essential transport property required for the effective utilisation of nanofluids in industrial systems as well as other applications. Viscosity influences significantly the pumping power and heat transfer effectiveness in a thermal system since Reynolds and Prandtl numbers are functions of viscosity. In this study, the optimum energy required for the preparation of MgO-ethylene glycol (MgO-EG) nanofluids was determined by varying the ultrasonication energy input into the preparation process. The uniformly dispersed nanofluids were characterised and the viscosity measurements were carried out as a function of temperature (20 to Therefore, new correlation is proposed using the method of dimensional analysis and considering essential factors, including nanoparticle size, volume fraction temperature, capping layer thickness, viscosity of the base fluid, the density of base fluid and the density of nanofluid as input parameters.Furthermore, genetic algorithm-polynomial neural network (GA-PNN) and fuzzy C-means clusteringbased adaptive neuro-fuzzy inference system (FCM-ANFIS) was used to model the effective viscosity of the MgO-EG nanofluids considering the parameters mentioned above. The results of all the modelling techniques showed good agreement with the experimental data.

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“…[8,9] Assuming that the sphericity of copper nanoparticles is of 0.3, the thermal conductivity of water can be enhanced by a factor of 1.5 at the nanoparticle volume fraction of 5%. Kang et al [10] have explained that the effects of particle volume fraction, size and shape result in high shear viscosity and play a significant role on the thermal conductivity ratio for the nanofluid. Ravi et al [11] have also demonstrated that the convection caused by the Brownian movement of these nanoparticles is primarily responsible for the enhancement of the thermal conductivity at different particle volume fraction.…”

Section: Introductionmentioning

confidence: 99%

Near-Field Nanofluid Concentration Measurement by Rayleigh Particle Scattering Bragg Grating Evanescent Wave

2014

International Journal of Optomechatronics

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We report an approach to detect near-field nanofluid concentration by scattering Bragg grating evanescent wave (EW). Since the suspended nanoparticles can enhance the scattering intensity of the EW from the thinned and tapered fiber with Bragg grating, the reflectance ratio of Bragg grating is dependent on the corresponding refractive index (RI) of the nanofluid at different nanoparticle volume fraction. A critical reflectance ratio measurement identifies the nanofluid concentration. Theory and simulation of scattering Bragg grating EW was analyzed. The scattering Bragg grating EW fiber sensing probe was designed and fabricated by the wet chemical etching method, and calibration was made by several chemical solutions without suspended nanoparticles. The example application of the nanofluid containing dispersed 40 nm SiO 2 nanoparticles demonstrates the feasibility. The reflectance ratio decreases by over 3.2% with the nanofluid concentration increasing from 0.25 wt.% to 4 wt.%, while the temperature disturbance can be negligible.

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Estimation of Thermal Conductivity of Nanofluid Using Experimental Effective Particle Volume

Cited by 398 publications

References 12 publications

Thermal conductivity measurements of nanofluids

Thermal conductivity measurements of nanofluids

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

Near-Field Nanofluid Concentration Measurement by Rayleigh Particle Scattering Bragg Grating Evanescent Wave

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