Mohsen Sharifpur

A Review of Thermal Conductivity Models for Nanofluids

Nanofluids, as new heat transfer fluids, are at the center of attention of researchers, while their measured thermal conductivities are more than for conventional heat transfer fluids. Unfortunately, conventional theoretical and empirical models cannot explain the enhancement of the thermal conductivity of nanofluids. Therefore, it is important to understand the fundamental mechanisms as well as the important parameters that influence the heat transfer in nanofluids. Nanofluids’ thermal conductivity enhancement consists of four major mechanisms: Brownian motion of the nanoparticle, nanolayer, clustering, and the nature of heat transport in the nanoparticles. Important factors that affect the thermal conductivity modeling of nanofluids are particle volume fraction, temperature, particles size, pH, and the size and property of nanolayer. In this paper, each mechanism is explained and proposed models are critically reviewed. It is concluded that there is a lack of a reliable hybrid model that includes all mechanisms and influenced parameters for thermal conductivity of nanofluids. Furthermore, more work needs to be conducted on the nature of heat transfer in nanofluids. A reliable database and experimental data are also needed on the properties of nanoparticles.

The viscosity of nanofluids : a review of the theoretical, empirical, and numerical models

The enhanced thermal characteristics of nanofluids have made it one of the most raplidly growing research areas in the last decade. Numerous researches have shown the merits of nanofluids in heat transfer equipment. However, one of the problems is the increase in viscosity due to the suspension of nanoparticles. This viscosity increase is not desirable in the industry, especially when it involves flow, such as in heat exchanger or microchannel applications where lowering pressure drop and pumping power are of significance. In this regard, a critical review of the theoretical, empirical, and numerical models for effective viscosity of nanofluids is presented. Furthermore, different parameters affecting the viscosity of nanofluids such as nanoparticle volume fraction, size, shape, temperature, pH, and shearing rate are reviewed. Other properties such as nanofluid stability and magnetorheological characteristics of some nanofluids are also reviewed. The important parameters influencing viscosity of nanofluids are temperature, nanoparticle volume fraction, size, shape, pH, and shearing rate. Regarding the composite of nanofluids, which can consist of different fluid bases and different nanoparticles, different accurate correlations for different nanofluids need to be developed. Finally, there is a lack of investigation into the stability of different nanofluids when the viscosity is the target point.

Parametric Analysis of Effective Thermal Conductivity Models for Nanofluids

There is a lack of reported research on comprehensive hybrid models for the effective thermal conductivity of nanofluids that takes into consideration all major mechanisms and parameters. The major mechanisms are the nanolayer, Brownian motion and clustering. The recognized important parameters can be the volume fraction of the nanoparticles, temperature, particle size, thermal conductivity of the nanolayer, thermal conductivity of the base fluid, PH of the nanofluid, and the thermal conductivity of the nanoparticle. Therefore, in this work, a parametric analysis of effective thermal conductivity models for nanofluids was done. The impact of the measurable parameters, like volume fraction of the nanoparticles, temperature and the particle size for the more sited models, were analyzed by using alumina-water nanofluid. The result of this investigation identifies the lack of a hybrid equation for the effective thermal conductivity of nanofluids and, consequently, more research is required in this field.Copyright

Investigation Into Effective Viscosity, Electrical Conductivity, and pH of γ-Al2O3-Glycerol Nanofluids in Einstein Concentration Regime

Nanofluids have shown great promise in the design of heat transfer equipment. In this study, the viscosity of γ-Al2O3 glycerol nanofluids was investigated in the Einsteins volume concentration regime (≤2%). The effect of temperature, volume concentration, electrical conductivity, and pH was investigated at a constant shear rate. The nanoparticles were 20–30 nm. γ-Al2O3 glycerol nanofluids samples were prepared, followed by ultrasonication at two different time period of 3 and 6 h. The effects of volume concentration on the effective viscosity, electrical conductivity, and pH were all monitored in the temperature range of 20–70°C. It was found that classical models underpredicted the experimental data while an empirical model overpredicted the experimental data. Furthermore, the electrical conductivity and pH were significantly affected by temperature and volume fraction.

Parametric Analysis of Effective Viscosity Models for Nanofluids

Viscosity is an important consideration in the application of nanofluids as heat transfer fluids. Various models have been developed to predict the viscosity of nanofluids. The accuracy of these models is of important benefit in determining the rheological performance of nanofluids, particularly in conditions which vary continuously. In this paper, a parametric analysis is undertaken to investigate the degree of variability between empirical data and model predictions. It was found that there is high variability in the compared results, which suggests that a wide range of constitutive factors need to be incorporated into the models in order to account adequately for the rheological behaviour of nanofluids.© 2012 ASME

Influence of ultrasonication energy on the dispersion consistency of Al2O3–glycerol nanofluid based on viscosity data, and model development for the required ultrasonication energy density

ABSTRACT Achieving homogenised and stable suspensions has been one of the important research topics in nanofluid investigations. Preparing nanofluids, especially from the two-step method, is often accompanied with varying degrees of agglomerations depending on some parameters. These parameters include the physical structure of the nanoparticle, the prevalent particle charge, the strength of van der Waals forces of attraction and repulsiveness strength. Amongst the methods of deagglomeration, the use of ultrasonic vibration is most popular for achieving uniform dispersion. However, there are very few works related to its effect on the thermo-physical properties of nanofluids, and above all, standardising the minimum required ultrasonication time/energy for nanofluids synthesis. In this work, the optimum energy required for uniform and initially stable nanofluid has been investigated through experimental study on the combined influence of ultrasonication time/energy, nanoparticle size, volume fraction and temperature on the viscosity of alumina–glycerol nanofluids. Three different sizes of alumina nanoparticles were synthesised with glycerol using ultrasonication-assisted two-step approach. The viscosities of the nanofluid samples were measured between temperatures of 20–70 °C for volume fractions up to 5%. Based on the present experimental results, the viscosity characteristics of the nanofluid samples were dependent on particle size, volume fraction and working temperature. Using viscometry, the optimum energy density required for preparing homogenous nanofluid was obtained for all particle sizes and volume fractions. Finally, an energy density model was derived using dimensionless analysis based on the consideration of nanoparticle binding/interaction energy in base fluid, particle size, volume fraction, temperature and other base fluid properties. The models empirical constants were obtained using nonlinear regression based on the present experimental data.

Factors affecting the pH and electrical conductivity of MgO–ethylene glycol nanofluids

The pH and electrical conductivity are important properties of nanofluids that have not been widely studied, especially with regard to temperature and ultrasonication energy. To study the factors that affect the pH and electrical conductivity of magnesium oxide–ethylene glycol (MgO–EG) nanofluid, the effects of temperature, volume fraction, particle size and ultrasonication energy were investigated. Two different sizes of MgO were dispersed in EG base fluid up to the volume fraction of 3%, and the pH and electrical conductivity were monitored between the temperatures of 20 and 70∘C. Characterization by transmission electron microscopy and size analyses revealed the morphology and sizes of the nanoparticle samples. The pH values dropped consistently with the increase of temperature, while electrical conductivity value increased with the increase of temperature. The experimental result showed that the increase in the MgO volume fraction increased both the pH and electrical conductivity values of the MgO–EG nanofluid. There was no recognizable influence of ultrasonication energy density on the pH and electrical conductivity of the nanofluid; therefore, it was concluded that temperature, volume fraction and particle size are the predominant factors affecting both the pH and electrical conductivity of MgO–EG nanofluid within the present experimental conditions.

A Review and Parametric Investigation Into Nanofluid Viscosity Models

The degree of variability between theoretical and empirical nanofluid viscosity model predictions and relevant experimental data is examined in this work. Results confirm a high degree of variability in the compared data; with some observed inconsistencies in the model formulations and the predicted data, consequently, a range of constitutive fac-tors need to be incorporated into the models in order to accurately predict the rheologi-cal behavior of nanofluids in different use conditions. Notably, conducting broad theoretical studies and empirical investigations into the rheological behavior of nano-fluids incorporating the fundamental parametric variables can plausibly lead to near-generalized models.

Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids

By using on Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as experimental data, a model was established for the prediction of the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In the ANFIS the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. In the development of the model, the empirical data was divided into train and test sections. The ANFIS network was instructed by eighty percent of the experimental data and the remaining data (twenty percent) were considered for benchmarking. The results which were obtained by the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) model were in good agreement with the experimental results.© 2012 ASME

Aggregation study of Brownian nanoparticles in convective phenomena

The explanation of abnormal enhancement of transported energy in colloidal nanoparticles in a liquid has sparked much interest in recent years. The complexity comes from the inter-particle phenomenon and cluster formation. The process of nanoparticle aggregation, which is caused by convective phenomena and particle-to-particle interaction energy in a flow, is investigated in this research. Therefore, the probability of collision and cohesion among clusters is modelled, as stated in this research. ANSYS-Fluent 17 CFD tools are employed to implement a new method of nanoparticle aggregation, new essential forces, new heat law and cluster drag coefficient. The importance of the interaction forces is compared to drag force, and essential forces are considered in coupling between nanoparticles and fluid flow. An important parameter is defined for the surface energy density regarding the attractive energy between the double layer and surrounding fluid to capture the cohesion of particles. Particles’ random migration is also presented through their angular and radial displacement. The analyses for interactions show the significance of Brownian motion in both particles’ migration and coupling effects in the fluid. However, nanoparticles are pushed away from walls due to repulsive forces, and Brownian motion is found to be effective mainly on angular displacement around the tube centreline. The attractive energy is found to be dominant when two clusters are at an equal distance. Hence, the cluster formation in convective regions should be taken into account for modelling purposes. A higher concentrated region also occurs midway between the centreline and the heated wall.