Empirical correlating equations for predicting the effective thermal conductivity and dynamic viscosity of nanofluids

Corcione, Massimo

doi:10.1016/j.enconman.2010.06.072

Cited by 1,045 publications

(445 citation statements)

References 48 publications

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“…[36] which considered particle volume fraction and agglomeration, and Corcione [39] which considered particle size, base fluid properties and volume fraction, both over predicted the present experimental data.…”

Section: Dimensional Analysissupporting

confidence: 65%

“…In the prediction made by Corcione [39], it can be seen that for the smaller nanoparticle size the deviation from experimental data is also higher than for samples with bigger nanoparticle size. When the correlation in Eq.…”

Section: Dimensional Analysismentioning

confidence: 96%

“…4) of the form of Batchelor's model [19]. Corcione [39] on the other hand considered nanoparticles size, particle volume fraction and base fluid properties such as molecular mass and density to derive the empirical correlation of Eq. (5).…”

Section: Theoretical Modelsmentioning

confidence: 99%

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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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Section: Dimensional Analysissupporting

confidence: 65%

Section: Dimensional Analysismentioning

confidence: 96%

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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

View full text Add to dashboard Cite

show abstract

“…Corcione [89,90], offered an empirical relation for the effective thermal conductivity of nanofluids based on literature experimental data with a 1.86% standard deviation of error. His …”

Section: Hybrid Model and Other Effectsmentioning

confidence: 99%

A Review of Thermal Conductivity Models for Nanofluids

Aybar

Sharifpur

Azizian

et al. 2015

Heat Transfer Engineering

203

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“…Their proposed thermal conductivity and viscosity models were as functions of temperature, nanoparticles diameter size, and nanoparticles volume fractions. Corcione [19] also investigated the e ects of di erent parameters on the properties of nano uids and introduced two empirical correlations to predict the e ective thermal conductivity and dynamic viscosity of various nano uids. In addition to the above authors, Patel et al [20] presented a correlation in order to calculate the e ective thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

Effects of uncertainties of conductivity models on mixed convection for Al2O3-water nano fluid

Abbasian

Yazdeli

2016

Scientia Iranica

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Abstract. The present numerical study aims to investigate the e ects of uncertainties of di erent conductivity models on mixed convection uid ow and heat transfer in a square cavity lled with Al2O3-water nano uid. The left and right vertical sides of enclosure are maintained at high and low constant temperatures, respectively, while the bottom and top horizontal sides of the enclosure are kept insulated. Furthermore, the right wall moves from down to up with a constant velocity, Vp. To approximate the nano uid e ective thermal conductivity, ve most commonly used models, namely, Maxwell, Khanafer and Vafai, Corcione, Chon et al., and Patel et al., are employed. Finite volume method and SIMPLER algorithm are used in order to discretize the governing equations. Simulations are performed for nanoparticles volume fraction ranging from 0 to 0.05 and Richardson number ranging between 0.01 and 100. The results indicate that there are signi cant di erences between the average Nusselt numbers predicted by the ve employed conductivity models.

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Empirical correlating equations for predicting the effective thermal conductivity and dynamic viscosity of nanofluids

Cited by 1,045 publications

References 48 publications

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

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

A Review of Thermal Conductivity Models for Nanofluids

Effects of uncertainties of conductivity models on mixed convection for Al2O3-water nano fluid

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