Enhancing thermal conductivity of fluids with nano-particles
01 Jan 1995-Vol. 231, pp 99-105
About: The article was published on 1995-01-01 and is currently open access. It has received 7263 citations till now. The article focuses on the topics: Thermal conductivity & Nanoparticle.
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TL;DR: A review of the progress made in the area of nanofluids preparation and applications in various heat transfer devices such as solar collectors, heat exchangers, refrigeration systems, radiators, thermal storage systems and electronic cooling is presented in this paper.
Abstract: The field of nanofluids has received interesting attention since the concept of dispersing nanoscaled particles into a fluid was first introduced in the later part of the twentieth century This is evident from the increased number of studies related to nanofluids published annually The increasing attention on nanofluids is primarily due to their enhanced thermophysical properties and their ability to be incorporated into a wide range of thermal applications ranging from enhancing the effectiveness of heat exchangers used in industries to solar energy harvesting for renewable energy production Owing to the increasing number of studies relating to nanofluids, there is a need for a holistic review of the progress and steps taken in 2019 concerning their application in heat transfer devices This review takes a retrospective look at the year 2019 by reviewing the progress made in the area of nanofluids preparation and the applications of nanofluids in various heat transfer devices such as solar collectors, heat exchangers, refrigeration systems, radiators, thermal storage systems and electronic cooling This review aims to update readers on recent progress while also highlighting the challenges and future of nanofluids as the next-generation heat transfer fluids Finally, a conclusion on the merits and demerits of nanofluids is presented along with recommendations for future studies that would mobilise the rapid commercialisation of nanofluids
181 citations
TL;DR: Experimental results show that nanofluids with low concentration of Cu, CuO, or carbon nanotube (CNT) have considerably higher thermal conductivity than identical base liquids, and dynamic effect, such as nanoparticle dispersion, may effectively augment the system performance.
Abstract: In this study, enhancements of thermal conductivities of ethylene glycol, water, and synthetic engine oil in the presence of copper (Cu), copper oxide (CuO), and multi-walled carbon nanotube (MWNT) are investigated using both physical mixing method (two-step method) and chemical reduction method (one-step method). The chemical reduction method is, however, used only for nanofluid containing Cu nanoparticle in water. The thermal conductivities of the nanofluids are measured by a modified transient hot wire method. Experimental results show that nanofluids with low concentration of Cu, CuO, or carbon nanotube (CNT) have considerably higher thermal conductivity than identical base liquids. For CuO-ethylene glycol suspensions at 5 vol.%, MWNT-ethylene glycol at 1 vol.%, MWNT-water at 1.5 vol.%, and MWNT-synthetic engine oil at 2 vol.%, thermal conductivity is enhanced by 22.4, 12.4, 17, and 30%, respectively. For Cu-water at 0.1 vol.%, thermal conductivity is increased by 23.8%. The thermal conductivity improvement for CuO and CNT nanofluids is approximately linear with the volume fraction. On the other hand, a strong dependence of thermal conductivity on the measured time is observed for Cu-water nanofluid. The system performance of a 10-RT water chiller (air conditioner) subject to MWNT/water nanofluid is experimentally investigated. The system is tested at the standard water chiller rating condition in the range of the flow rate from 60 to 140 L/min. In spite of the static measurement of thermal conductivity of nanofluid shows only 1.3% increase at room temperature relative to the base fluid at volume fraction of 0.001 (0.1 vol.%), it is observed that a 4.2% increase of cooling capacity and a small decrease of power consumption about 0.8% occur for the nanofluid system at a flow rate of 100 L/min. This result clearly indicates that the enhancement of cooling capacity is not just related to thermal conductivity alone. Dynamic effect, such as nanoparticle dispersion may effectively augment the system performance. It is also found that the dynamic dispersion is comparatively effective at lower flow rate regime, e.g., transition or laminar flow and becomes less effective at higher flow rate regime. Test results show that the coefficient of performance of the water chiller is increased by 5.15% relative to that without nanofluid.
180 citations
Cites methods from "Enhancing thermal conductivity of f..."
...Jang SP, Choi SUS: Role of Brownian motion in the enhanced thermal conductivity of nanofluids....
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...Choi SUS, Zhang ZG, Yu W, Lockwood FE, Grulke EA: Anomalous thermal conductivity enhancement in nanotube suspensions....
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...Yu W, France DM, Routbort JL, Choi SUS: Review and comparison of nanofluid thermal conductivity and heat transfer enhancements....
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...Lee S, Choi SUS, Li S, Eastman JA: Measuring thermal conductivity of fluids containing oxide nanoparticles....
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...Choi SUS: Enhancing thermal conductivity of fluids with nanoparticles....
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TL;DR: In this paper, the effect of mean diameter of nanoparticles on the convective heat transfer and pressure drop studied at nanoparticle volume concentration from 0.01 to 0.02 by volume.
180 citations
TL;DR: In this paper, an experimental work on the measurement of effective electrical conductivity of aqueous suspensions of aluminum oxide nanoparticles (nanofluids) was performed both as a function of volume fraction and temperature.
180 citations
TL;DR: In this paper, a comprehensive review of the most important modeling works on viscosity of nanofluids including theoretical models, empirical correlations, and computer-aided models is conducted.
Abstract: Viscosity of nanofluids can significantly affect pumping power, pressure drop, workability of the nanofluid as well as its convective heat transfer coefficient. Experimental measurements of this property for different nanoparticles and base fluids at various temperatures is cumbersome and expensive. In this communication, a comprehensive review of the most important modeling works on viscosity of nanofluids including theoretical models, empirical correlations, and computer-aided models is conducted. Next, four multilayer perceptron (MLP) models optimized with Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled conjugate gradient (SCG), and Resilient Backpropagation (RB), two radial basis function (RBF) neural network models optimized with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and one least square support vector machine (LSSVM) model optimized with coupled simulated annealing (CSA) were developed for the prediction of nanofluid viscosity based on 3144 data points. These data sets include 42 nanofluid systems under a wide range of operating conditions; including temperature from −35 to 80 °C, particle volume fraction from 0% to 10%, nanoparticle size from 4.6 to 190 nm, and viscosity of base fluid from 0.24 to 452.6 cP. Then, these seven models were combined in a single model using a committee machine intelligent system (CMIS). The proposed CMIS predicts all of the data with excellent accuracy with an average absolute relative error of less than 4%. Furthermore, the developed model was compared with five theoretical models and four empirical correlations through statistical and graphical error analyses. The results demonstrate that the proposed CMIS model significantly outperforms all of the existing models and correlations in terms of accuracy and range of validity. Finally, the quality of the experimental data was examined both graphically and statistically and the results suggested good reliability of the experimental data.
179 citations