On the dependence of the viscosity coefficient of nanofluids on particle size and temperature

Rudyak, V. Ya.; Dimov, S. V.; Kuznetsov, V. V.

doi:10.1134/s1063785013090125

Cited by 47 publications

(9 citation statements)

References 12 publications

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“…In the case of polydisperse systems interactions were much more complex [6]. The same conclusions presented Rudyak et al examining nano-fluids based on ethylene glycol with silicon oxide [7]. Maranzano and Wagner evaluated the influence of the size of the dispersed particles and the concentration of the particles on the shear stress value in viscosity measurements [8].…”

Section: Introductionsupporting

confidence: 55%

Influence of Liquid Paraffin, White Soft Paraffin and Initial Hydration on Viscosity of Corticosteroid Cream

Szewczyk¹,

Karlowicz–Bodalska

Han³

et al. 2014

Trop. J. Pharm Res

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Section: Introductionsupporting

confidence: 55%

Influence of Liquid Paraffin, White Soft Paraffin and Initial Hydration on Viscosity of Corticosteroid Cream

Szewczyk¹,

Karlowicz–Bodalska

Han³

et al. 2014

Trop. J. Pharm Res

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“…An enhancement of 60 % in viscosity has been observed at 5 % volume concentration. Rudyak et al [22] studied the effects of nanoparticle size (18.1, 28.3 and 45.6 nm), temperature (20-60°C) and volume concentration (0.2-8 %) on the viscosity of SiO 2 -ethylene glycol nanofluids. It has been observed that the smaller the nanoparticles, the higher the viscosity.…”

Section: Introductionmentioning

confidence: 99%

Effect of volume concentration and temperature on viscosity and surface tension of graphene–water nanofluid for heat transfer applications

Ahammed

Asirvatham

Wongwises

2015

J Therm Anal Calorim

164

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In the present study, the effect of volume concentration (0.05, 0.1 and 0.15 %) and temperature (10-90°C) on viscosity and surface tension of graphene-water nanofluid has been experimentally measured. The sodium dodecyl benzene sulfonate is used as the surfactant for stable suspension of graphene. The results showed that the viscosity of graphene-water nanofluid increases with an increase in the volume concentration of nanoparticles and decreases with an increase in temperature. An average enhancement of 47.12 % in viscosity has been noted for 0.15 % volume concentration of graphene at 50°C. The enhancement of the viscosity of the nanofluid at higher volume concentration is due to the higher shear rate. In contrast, the surface tension of the graphenewater nanofluid decreases with an increase in both volume concentration and temperature. A decrement of 18.7 % in surface tension has been noted for the same volume concentration and temperature. The surface tension reduction in nanofluid at higher volume concentrations is due to the adsorption of nanoparticles at the liquid-gas interface because of hydrophobic nature of graphene; and at higher temperatures, is due to the weakening of molecular attractions between fluid molecules and nanoparticles. The viscosity and surface tension showed stronger dependency on volume concentration than temperature. Based on the calculated effectiveness of graphene-water nanofluids, it is suggested that the graphene-water nanofluid is preferable as the better coolant for the real-time heat transfer applications.

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“…All the above analyses further demonstrate that the RBF neural network is one of the potential tools to quantitatively establish nonlinear relationships between inputs and outputs. Figure 7 compares the experimental viscosity ratio of Rudyak et al [15] with the predicted values of the RBF neural network for the SiO2-ethylene glycol nanofluids at T = 25 °C as a function of the nanoparticle volume fraction and diameter. It can be found from Figure 7a that the RBF predicted viscosity ratio of nanofluids are obviously enhanced with the increase of the SiO2 nanoparticle volume fraction and the decrease of the nanoparticle size, which are consistent with the experimental results.…”

Section: T (°C) φP (%) Experiments (P) Rbf Prediction (Q)mentioning

confidence: 99%

“…At a nanoparticle volume fraction of 1%, the viscosity of the base fluid could be enhanced by 2.9 times. Chen et al [12], Jamshidi et al [13], Kulkarni et al [14], Rudyak et al [15], Namburu et al [16], Lim et al [17], Chiam et al [18], and Li et al [19] respectively measured the viscosity of various ethylene glycol/water mixture based nanofluids with the effects of different factors. According to their experimental results, it was found that a suspension of nanoparticles could enhance the viscosity of the base fluid in different degrees.…”

Section: Introductionmentioning

confidence: 99%

Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network

Zhao

2017

Applied Sciences

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Abstract:In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids. A total of 216 experimental data involving CuO, TiO 2 , SiO 2 , and SiC nanoparticles were collected from the published literature to train and test the RBF neural network. The parameters including temperature, nanoparticle properties (size, volume fraction, and density), and viscosity of the base fluid were selected as the input variables of the RBF neural network. The investigations demonstrated that the viscosity ratio predicted by the RBF neural network agreed well with the experimental data. The root mean squared error (RMSE), mean absolute percentage error (MAPE), sum of squared error (SSE), and statistical coefficient of multiple determination (R 2 ) were respectively 0.04615, 2.12738%, 0.46007, and 0.99925 for the total samples when the Spread was 0.3. In addition, the RBF neural network had a better ability for predicting the viscosity ratio of nanofluids than the typical Batchelor model and Chen model, and the prediction performance of RBF neural networks were affected by the size of the data set.

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On the dependence of the viscosity coefficient of nanofluids on particle size and temperature

Cited by 47 publications

References 12 publications

Influence of Liquid Paraffin, White Soft Paraffin and Initial Hydration on Viscosity of Corticosteroid Cream

Influence of Liquid Paraffin, White Soft Paraffin and Initial Hydration on Viscosity of Corticosteroid Cream

Effect of volume concentration and temperature on viscosity and surface tension of graphene–water nanofluid for heat transfer applications

Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network

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