• Journal of Power System Engineering
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• v.17 no.2
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• pp.78-86
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• 2013

In this research, dilute colloidal suspension alumina nanofluids were prepared by dispersing alumina nanoparticles in DI water and ethylene glycol as base fluids. Particle size analyzer and TEM test results revealed that the size of the alumina nanofluids(3wt% and 5wt%) with dispersion time 3hrs were 46nm and 60nm respectively. Thermal conductivity of these alumina nanofluids was measured by means of hot wire technique using a LAMBDA system. For water based alumina nanofluids, thermal conductivity enhancement was from 2.29% to 3.06% with 5wt% alumina at temperatures ranging from 15 to $40^{\circ}C$. Whereas in case of ethylene glycol based alumina nanofluids under the same temperature range, thermal conductivity enhancement was from 9.6% to 10% with 5wt% alumina. An enhancement of 37% average convective heat transfer was achieved with 5wt% alumina nanofluids at Re of 1,100.

• Journal of ILASS-Korea
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• v.19 no.4
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• pp.174-181
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• 2014

This study investigates the effects of nanoparticle size and temperature on the thermal conductivity enhancement of water-based alumina ($Al_2O_3$) nanofluids, using the centrifuging method and relative centrifugal forces of differing magnitude to produce nanofluids of three different particles without involving any dispersants or surfactants. We determined the coupling dependency in thermal conductivity enhancement relative to nanoparticle size and temperature of the alumina nanofluids and also experimentally showed that the effect of temperature on thermal conductivity is strongly dependent on nanoparticle size. Also, our experimental data presented that the effective medium theory models such as the Maxwell model and Hasselman and Johnson model are not sufficient to explain the thermal conductivity of nanofluids since they cannot account for the temperature- and size-dependent nature of water-based alumina nanofluids.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.

• Nuclear Engineering and Technology
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• v.45 no.3
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• pp.335-346
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• 2013

The Critical Heat Flux (CHF) of water with dispersed alumina nanoparticles was measured for the geometry and flow conditions relevant to the In-Vessel Retention (IVR) situation which can occur during core melting sequences in certain advanced Light Water Reactors (LWRs). CHF measurements were conducted in a flow boiling loop featuring a test section designed to be thermal-hydraulically similar to the vessel/insulation gap in the Westinghouse AP1000 plant. The effects of orientation angle, pressure, mass flux, fluid type, boiling time, surface material, and surface state were investigated. Results for water-based nanofluids with alumina nanoparticles (0.001% by volume) on stainless steel surface indicate an average 70% CHF enhancement with a range of 17% to 108% depending on the specific flow conditions expected for IVR. Experiments also indicate that only about thirty minutes of boiling time (which drives nanoparticle deposition) are needed to obtain substantial CHF enhancement with nanofluids.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.10
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• pp.729-736
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• 2009

Pool boiling heat transfer and critical heat flux (CHF) of water-based nanofluids with alumina and titania nanoparticles of 0.01% by volume were investigated on a disk heater at saturated and atmospheric conditions. The experimental results showed that the boiling in nanofluids caused the considerable increase in CHF on the flat surface heater. It was revealed by visualization of the heater surface subsequent to the boiling experiments that a major amount of nanoparticles deposited on the surface during the boiling process. Pool boiling of pure water on the surface modified by such nanoparticle deposition resulted in the same CHF increases as what boiling nanofluids, thus suggesting the CHF enhancement in nanofluids was an effect of the surface modification through the nanoparticle deposition during nanofluid boiling. Possible reasons for CHF enhancement in pool boiling of nanofluids are discussed with surface property changes caused by the nanoparticle deposition.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.1
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• pp.16-21
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• 2013

Nanofluids are suspensions of metallic or nonmetallic nano powders in base liquid and can be employed to increase heat transfer rate in various applications. In this research the heat transfer characteristics has been experimented by alumina(50 nm)/water nano-fluids. Experimental results showed that the increasing the concentration of alumina nanofluids local and average heat transfer coefficient increased. The local heat transfer coefficient of 6 Wt% nanofluid was increased 37~46% than water at X/D=50~120. The average heat transfer coefficient of 6 Wt% nanofluid was rapidly increased than water at Reynolds number 1100~1300.

• Proceedings of the SAREK Conference
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• 2005.11a
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• pp.539-545
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• 2005

In this paper, the thermal characteristics of natural convection in a rectangular cavity with nanofluids such as water-based nanofluids containing alumina are theoretically investigated with a new model of the thermal conductivity for nanofluids presented by Jang and Choi and various models for effective viscosity. In addition, based on theoretical results, the effects of various parameters such as the volume fraction, the temperature, and the size of nanoparticles on free convective instability and heat transfer characteristics in a rectangular cavity with nanofluids are suggested.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.9 s.240
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• pp.1065-1073
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• 2005

The thermal conductivity of water- and ethylene glycol-based nanofluids containing alumina $(Al_2O_3)$, zinc oxide (ZnO) and titanium dioxide $(TiO_2)$ nanoparticles is measured by varying the particle diameter and volume fraction. The transient hot-wire method using an anodized tantalum wire for electrical insulation is employed for the measurement. The experimental results show that nanofluids have substantially higher thermal conductivities than those of the base fluid and the ratio of thermal conductivity enhancement increases linearly with the volume fraction. It has been found that the ratio of thermal conductivity enhancement increases with decreasing particle size but no empirical or theoretical correlation can explain the particle-size dependence of the thermal conductivity. This work provides, for the first time to our knowledge, a set of consistent experimental data over a wide range of nanofluid conditions and can therefore serve as a basis for developing theoretical models to predict thermal conduction phenomena in nanofluids.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.147-156
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• 2019

Numerical study was performed to investigate the convective heat transfer of Al₂O₃/water nanofluid flowing through the concentric double pipe counterflow heat exchangers. Hot fluid flowing through the inner pipe transfers its heat to cooling fluid flowing in the outer pipe. Effects of important parameters such as hot and cold volume flow rates, fluid type in the outer and inner pipes, and nanoparticles concentration on the heat transfer and flow characteristics are investigated. The results indicated that the heat transfer performance increases with increasing the hot and cold volume flow rates, as well as the particle concentrations. When both outer and inner pipes are nanofluids with 8% nanoparticle volume concentration, nanofluids showed up to 17% better heat transfer rate than basic fluids. Also, the average heat transfer coefficient of the base fluid for annulus-side improved by 31%. Approximately 20% enhancement in the heat exchanger effectiveness can be achieved with the addition of 8% alumina particles in base fluid. But, addition of nanoparticles to the base fluid enhanced friction factor by about 196%.