Effects of working temperature on thermo-physical properties and forced convection heat transfer of TiO 2 nanofluids in water – Ethylene glycol mixture

Azmi, W.H.; Hamid, Khalid; Mamat, Rizalman; Sharma, K.V.; Mohamad, Mahathir bin

doi:10.1016/j.applthermaleng.2016.06.106

Cited by 105 publications

(42 citation statements)

References 53 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Due to the fact that viscosity can significantly affect the flow internal resistance, inlet Reynolds number, and pressure drop, many experimental investigations have been carried out regarding the viscosity of different nanofluids. As reported by Azmi et al [10], the viscosity of the 40:60 (by volume ratio) ethylene glycol/water mixture could be increased obviously by dispersing TiO 2 nanoparticles. For example, the viscosity enhancement was about 12% when the nanoparticle volume fraction changed from 0.5% to 1.5%.…”

Section: Introductionsupporting

confidence: 52%

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

Zhao

2017

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionsupporting

confidence: 52%

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

Zhao

2017

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The relationship (4) of Gnielinski is very widely used, as well approximates the experimental data [17,[23][24][25]. In recent years, however, a new rise in popularity of power-type correlation to an approximation of the measurement data is observed [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…[26] also used electrically heat tubes to study hydrodynamically fully developed flow. Before starting the actual experiments with nanofluids, tests were conducted in which the working fluid was water [25] or a mixture of water and ethylene glycol [26]. The Reynolds number varied in the study of Azmi et al from 3,000 to 25,000.…”

Section: Introductionmentioning

confidence: 99%

Simple heat transfer correlations for turbulent tube flow

Taler

2017

E3S Web Conf.

View full text Add to dashboard Cite

Abstract. The paper presents three power-type correlations of a simple form, which are valid for Reynolds numbers range from 3·10 3 ≤ Re ≤ 10 6 , and for three different ranges of Prandtl number: 0.1 ≤ Pr ≤ 1.0, 1.0 < Pr ≤ 3.0, and 3.0 < Pr ≤ 10 3 . Heat transfer correlations developed in the paper were compared with experimental results available in the literature. The comparisons performed in the paper confirm the good accuracy of the proposed correlations. They are also much simpler compared with the relationship of Gnielinski, which is also widely used in the heat transfer calculations.

show abstract

“…It has been found that nanofluid can enhance heat transfer. Typical nanoparticles of metals, such as Al and Cu, and metal oxides such as TiO 2 , Fe 3 O 4 , Al 2 O 3 and CuO, have been used for nanofluid [1][2][3][4][5][6][7][8]. Further, nanofluids have also been used with other enhancing devices for the heat transfer process, such as wire coils, twisted tape and roughened heat exchanger surfaces [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Turbulent Convective Heat Transfer using a Microparticle Multiphase Flow

2020

View full text Add to dashboard Cite

The turbulent heat transfer enhancement of microfluid as a heat transfer medium in a tube was investigated. Within the Reynolds number ranging from 7000 to 23,000, heat transfer, friction loss and thermal performance characteristics of graphite, Al2O3 and CuO microfluid with the particle volume fraction of 0.25%–1.0% and particle size of 5 μm have been respectively tested. The results showed that the thermal performance of microfluids was better than water. In addition, the graphite microfluid had the best turbulent convective heat transfer effect among several microfluids. To further investigate the effect of graphite particle size on thermal performance, the heat transfer characteristics of the graphite microfluid with the size of 1 μm was also tested. The results showed that the thermal performance of the particle size of 1 μm was better than that of 5 μm. Within the investigated range, the maximum value of the thermal performance of graphite microfluid was found at a 1.0% volume fraction, a Reynolds number around 7500 and a size of 1 μm. In addition, the simulation results showed that the increase of equivalent thermal conductivity of the microfluid and the turbulent kinetic energy near the tube wall, by adding the microparticles, caused the enhancement of heat transfer; therefore, the microfluid can be potentially used to enhance turbulent convective heat transfer.

show abstract

Effects of working temperature on thermo-physical properties and forced convection heat transfer of TiO 2 nanofluids in water – Ethylene glycol mixture

Cited by 105 publications

References 53 publications

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

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

Simple heat transfer correlations for turbulent tube flow

Enhancement of Turbulent Convective Heat Transfer using a Microparticle Multiphase Flow

Contact Info

Product

Resources

About