https://orcid.org/0000-0001-8732-2229
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ABSTRACT
The quest to have an effective and efficient heat transfer fluids has been a subject of great research interest for several decades. Nanofluids are preeminent fluids that have garnered increased attention recently due to their enhanced thermal properties. Moreover, viscosity is an essential characteristic of nanofluids and for this reason, many research works have been conducted on this subject over the last few decades. In this work, attempt is made to present the viscosity model of nanofluids. In addition, efforts are put forth to examine the existing empirical, semiempirical, and analytical correlations utilized for the evaluation of viscosity of nanofluids. Furthermore, there are a number of factors influencing the viscosity of nanofluids which has necessitated this review work. These factors not only feature the nanoparticle concentration, sizes, and types, they also include the temperature of the nanofluid as well as the base fluid surfactant and stability. This review also discuss several industrial applications of nanofluids such as those used for electronic cooling, solar applications, heat exchangers, and automotive industry. The available experimental results show that the above mentioned parameters have considerable effect over viscosity of nanofluid. However, there is no general trend to describe the influence of particle sizes on increase in viscosity. The lowest enhancement in viscosity with increase in nanoparticle concentration in literature was graphite/engine oil nanofluid with improvement of 6.25% with volume concentration increasing from 1 to 5.5%. It has also been observed that many empirical models have been formulated on viscosity of nanofluid; however, there is no universally accepted model. This review leads to some directions for future research in nanofluids.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
| ANN | = | Artificial neural network |
| BR | = | Base fluid mixture ratio |
| BG | = | BioGlycol |
| CNT | = | Carbon nanotubes |
| CTAB | = | Cetyltrimethyl ammonium bromide |
| DIW | = | Deionized water |
| DW | = | Distilled water |
| EO | = | Engine oil |
| EG | = | Ethylene glycol |
| FCM-ANFIS | = | Fuzzy C-means clustering-based adaptive neuro-fuzzy inference system |
| GA-PNN | = | Genetic algorithm-polynomial neural network |
| HO | = | Hydraulic oil |
| MWCNT | = | Multi-walled carbon nanotube |
| MeOH | = | Methanol |
| ND | = | Nanodiamond |
| PG | = | Propylene glycol |
| R | = | Particle mixture ratio |
| SAE | = | Society of Automotive Engineers |
| SBDS | = | Sodium dodecylbenzene sulfonate |
| SDS | = | Sodium dodecyl sulphate |
| SLS | = | Sodium laurylsulfonate |
| SWCNT | = | Single walled carbon nanotubes |
| T | = | Temperature |
| W | = | Water |
| PVP | = | Polyvinylpyrrolidone |
| PEO | = | Polyethylene oxide |
| m | = | Consistency index |
| n | = | Power law index |
| dp | = | Diameter of a particle |
| EG/W | = | Ethylene glycol and water mixture |
| F-DWCNT | = | functionalized double walled carbon nanotubes |
| Greek Letters | = | |
| θ | = | Dimensionless temperature |
| ρ | = | Density |
| μ | = | Dynamic viscosity |
| φ | = | Nanoparticle concentration |
| ᅹ | = | Shear rate |
| τ | = | Shear stress |
| Subscripts | = | |
| bf | = | Base fluid |
| eff | = | Effective |
| p | = | Particle |
| THNF | = | Ternary hybrid nanofluid |
Additional information
Notes on contributors
Chidozie Ezekwem
Chidozie Ezekwem earned his Ph.D. in Mechanical Engineering in 2018 from the University of Ibadan. He is currently a lecturer in Mechanical Engineering at the University of Port Harcourt. His current research interests include computational fluid dynamics, heat transfer enhancement using nanofluids, and heat exchanger design in a variety of applications such as automobiles and oil and gas.