Comparison of Correlation Predicting Thermo Physical Properties of Nanofluids SIWAN - 2013 C. Selvam a*, D. Mohan Lal a, L. Godson Asirvatham b a Refrigeration & Air-Conditioning Division, Department of Mechanical Engineering, College of Engineering, Anna University, Chennai -600 025, Tamil Nadu, India b Department of Mechanical Engineering, Karunya University, Coimbatore, Tamil Nadu 641114, India Abstract Viscosity measurement Nanofluid with nanoparticles at low- volume fractions in liquid, enhances the thermal conductivity of the fluid. Thus, they are potentially useful for augmenting the heat transfer in thermal systems. Focusing mainly on low concentration with well-dispersed nanoparticles in water or ethylene glycol, many researchers have reviewed the measurement techniques for thermo physical properties, new theories as well as useful correlations that are available in open literature. This article presents the comparison between measured values of the thermal conductivity and viscosity of silver/de-ionized water nanofluids. The experimental data is based on a study in which the silver/de-ionized water with volume concentration ranging from 0.3% to 1.2% and temperature ranging from 500C to 900C was considered. It is seen that the measured thermal conductivity and viscosity of nanofluids is much higher than the values predicted through correlations. The method chosen for the present study is the capillary viscometer, and more specifically the Cannon-Fenske Opaque, also called as the Reverse-Flow Viscometer is used to measure the kinematic viscosity of the silver/water nanofluid The glass viscometer size has a coefficient of calibration, which is multiplied by the time taken for the fluid sample to pass between the different marks on the viscometer to achieve kinematic viscosity This is then converted into dynamic viscosity by multiplying it with the density of the fluid Then, the measured viscosities of nanofluids were compared with those obtained from the existing well-known models, as follows The effective dynamic viscosity of nanofluids can be calculated using Einstein’s equation for a viscous fluid containing a dilute suspension of small, rigid, spherical particles. Brinkman et al [8] extended the relation for the viscosity of nanofluids to a more generalized form because the above relation has been found valid for relatively low particle loading (φ≤ 2%). Batchelor et al. proposed a correlation to predict the viscosity of nanofluids with spherical-shaped nanoparticles, which is defined as: Wang et al [4] conducted experiments to find the viscosity of water based nanofluids, which is defined as:   Introduction Heating and cooling requirements are continually increasing in many industrial sectors including power generation, chemical production, air-conditioning, transportation, solar water heating and microelectronics. In many industrial applications the conventional fluids, such as water, oils and ethylene glycol are normally used as heat transfer fluids. The use of solid (nano) particles as an additive suspended into the base fluid is a technique for the enhancement of thermal conductivity, Viscosity and heat transfer coefficient. A nanofluid is a term first coined by Choi (1995) to denote a new class of heat transfer fluid that exhibits thermal properties superior to those of the conventional fluids Heat transfer systems that consist of nanofluids can dissipate extremely large volumetric thermal loads that are well beyond the feasible range of conventional fluids. Hence, nanofluids have great potential for heat transfer enhancement and are highly suited to application in practical heat transfer processes Preparation of nanofluids Results and discussion In the present study, a two-step method is used to prepare the silver-water nanofluids Silver nanopowder were dispersed into water The ultrasonic vibration is carried out for 5 hours using the ultrasonic processor Since the purity of the nanofluid is important, the silver nanoparticles are directly mixed with water with no additives Then, the prepared nanofluids are stirrer well for 5 hours in the magnetic stirrer Thermal conductivity measurement Fig.2- variation of Thermal conductivity of ag–water nanofluids with temperature and volume fraction Fig. 3- Comparison of the measured thermal conductivities with those obtained from various correlations Transient hot wire method is the most appropriate and widely used method to determine the thermal conductivity of liquids. A constant current is supplied to the wire (platinum) to generate the essential temperature rise. The wire serves as both the heat source and the temperature sensor. The wire is surrounded by a liquid, whose thermal conductivity is to be measured by Fig.4- Viscosity of silver–water nanofluids as a function of temperature and volume fraction Fig. 5- Comparison of the measured viscosity with those obtained from various correlations Conclusions The present study experimentally investigated the effective thermal conductivity and effective viscosity of silver–water nanofluids with particle volume concentrations of 0.3, 0.4, 0.6, 0.8, 0.9, and 1.2 vol% and temperatures ranging from 50 to 900C From the thermal conductivity measurement, the results show that the thermal conductivity of nanofluids increases with increase in the pure metal particle volume concentration and temperature. It is also observed that the existing correlations for predicting the thermal conductivity of nanofluids give lower values than the experimental values. In contrast with thermal conductivity data, the viscosity of nanofluids significantly decreases with increase in temperature and increases with increase in the pure metal particle volume concentration. Moreover, the existing correlations for calculating the viscosity of nanofluids are found to be not suitable for the nanofluids tested. S. Lee, S. Choi, S. Lee, and J. Eastman, Measuring Thermal Conductivity of Fluids Containing Oxide Nanoparticles, Journal of Heat Transfer, Vol. 121, pp. 280–289, 1999. R.L. Hamilton and O.K. Crosser, Thermal Conductivity of Heterogeneous Two-Component Systems, I & EC Fundamentals, Vol. 1, pp. 187–191, 1962. X. Wang, X. Xu, and S.U.S. Choi, Thermal Conductivity of Nanoparticles-Fluid Mixture, Journal of Thermophysics and Heat Transfer, Vol. 13, No. 4, pp. 474–480, 1999. E.V. Timofeeva, A.N. Gavrilov, J.M. McCloskey, and Y.V. Tolmachev, Thermal Conductivity and Particle Agglomeration in Alumina Nanofluids: Experiment and Theory,Physical Review, Vol. 76, pp. 061203, 2007. F.J. Wasp, Solid-Liquid Slurry Pipeline Transportation, Trans. Tech, Publications Inc.,Berlin, 1977 D.A. Drew and S.L. Passman, Theory of Multi Component Fluids, Springer, Berlin, 1999. H.C. Brinkman, The Viscosity of Concentrated Suspensions and Solution, Journal of Chemical Physics, Vol. 20, pp. 571–581, 1952 G.K. Batchelor, The Effect of Brownian Motion on the Bulk Stress in a Suspension ofSpherical Particles, Journal of Fluid Mechanics, Vol. 83, No. 1, pp. 97–117, 1977. Fig.1-Transient Hot wire set up The measured thermal conductivity of nanofluids is used for comparison with those obtained from the existing correlations, which are defined as follows. Hamilton and Crosser et al [3] proposed a model for calculating the thermal conductivity of Nanofluids for spherical and non-spherical (cylindrical) particles where n=3 for spheres, n=6 for cylinders which is expressed in the following form: Timofeeva et al [5] suggested the effective medium theory to calculate thermal conductivity of nanofluids, which is expressed as follows: Wasp et al [6] proposed a model for calculating the thermal conductivity of nanofluids, which is expressed as follows. References Nomenclature k – Thermal conductivity, W/mK q heat flux, W/m T2-final temperature, 0C T1- initial temperature, 0C t – time, s Greek Symbols γ-particle size, nm µ-Dynamic viscosity, mPa.s ν-Kinematic viscosity, m2/s ρ – density, kg/m3 φ – Volume fraction Subscripts nf - nanofluid p - particle w - water