Temperature and particle-size dependent viscosity data for water-based nanofluids – Hysteresis phenomenon
Introduction
Nanofluids, two-phase mixtures composed of very fine particles in suspension in a continuous and saturated liquid (water, ethylene glycol, engine oil), may constitute a very interesting alternative for advanced thermal applications (Lee and Choi, 1996, Chein and Huang, 2005). It has been found that important heat transfer enhancement may be achieved by using nanofluids instead of conventional fluids; furthermore, some oxide nanoparticles exhibit excellent dispersion properties in traditional cooling liquids. In spite of their remarkable features, few results on nanofluids use in confined flow situations have been published (see Daungthongsuk and Wongwises (2007) for a partial review). Pak and Cho, 1998, Li and Xuan, 2002 provided the first empirical correlation for computing Nusselt numbers in laminar and turbulent tube flows using water-based nanofluids. Others have considered the use of nanofluids in microchannel heat sinks (Chein and Huang, 2005). Recent publications (Ben Mansour et al., 2006, Maïga et al., 2005, Maïga et al., 2006, Palm et al., 2004, Roy et al., 2006a) confirmed the heat transfer enhancement due to nanofluids in tube flow and in radial flow between heated disks.
Research efforts have mostly been concerned with the characterization of thermal and physical properties of nanofluids; a good proportion of published studies is of an experimental nature and focuses on the determination of effective thermal conductivities. A review of relevant literature (see in particular Eastman et al., 2004, Murshed et al., 2005, Roy et al., 2006b) has shown an important data dispersion for thermal conductivity obtained from various sources. Furthermore, these data were concerned only with low particle concentrations below 5% in volume. The data dispersion mentioned above may be attributed to various factors such as measuring techniques, particle size and shape, as well as particle clustering and sedimentation. In spite of this, it is clear that the thermal properties of nanofluids are considerably higher than those of the ‘conventional’ base fluids (Wang et al., 1999, Eastman et al., 1999, Eastman et al., 2001). Apart from the pioneering works by Masuda et al. (1993), other relevant and important published results for nanofluid thermal conductivities include those by Choi, 1995, Pak and Cho, 1998, Lee et al., 1999, Xuan and Li, 2000, Murshed et al., 2005, Liu et al., 2006. Some of the researchers also considered the effect of particle aggregation and interfacial nanolayer (Xuan et al., 2003, Xie et al., 2005). It should be mentioned that there exist, so far, very limited data concerning the temperature effect on nanofluid thermal conductivities (Masuda et al., 1993, Das et al., 2003, Putra et al., 2003). Although the significant dependence of nanofluid thermal conductivity on temperature has clearly been shown, the amount of data remains very limited. The present authors have recently attempted to measure thermal conductivities for alumina–water nanofluids with particle concentrations ranging from 1% to nearly 9% (Roy et al., 2006b). Regarding the modeling of nanofluid effective thermal conductivity, one should mention the recent and interesting models proposed by Koo and Kleinstreuer, 2005, Chon et al., 2005, taking into account effects due to both temperature and particle size. It is worth noting that the differences in modeling nanofluid properties can lead to contradictory results regarding the thermal performance of nanofluids (Ben Mansour et al., 2007, Polidori et al., in press).
Regarding the nanofluid viscosity, the lack of data in the literature is even more striking. Masuda et al. (1993) were likely the first to measure the viscosity of several water-based nanofluids for temperatures ranging from room condition to 67 °C. Pak and Cho (1998) followed with viscosity data obtained for Al2O3–water nanofluid and two particle concentrations. Wang et al. (1999) obtained, using three different preparation methods, some data for Al2O3–water and Al2O3–ethylene glycol mixtures at ambient temperature. Putra et al. (2003) have also provided results showing the temperature effect on Al2O3–water nanofluid viscosity for two particle concentrations, namely 1% and 4%. Most recently, Maré et al. (2006), using a Brookfield viscometer with rotating cylinder, obtained some new temperature-dependent viscosity data for Al2O3–water at relatively high particle concentrations. To our knowledge, there exist no other data regarding nanofluids dynamic viscosity, a property of crucial importance for all thermal applications involving fluids.
In this paper, we present extensive measurements of the dynamic viscosities for three different water-based nanofluids, Al2O3–water with 36 nm and 47 nm particles, and CuO–water with 29 nm particles, for temperatures varying from room conditions to almost 75 °C.
Section snippets
Estimation of nanofluid viscosities
From the theoretical point of view, a nanofluid represents a fascinating new challenge to researchers in fluid dynamics and heat transfer because of the fact that it appears very difficult, if not practically impossible, to formulate any theory that can reasonably predict behaviours of a nanofluid by considering it as a multi-component fluid (Xuan and Roetzel, 2000). Yet, since a nanofluid is a two-phase fluid, one may expect that it would have common features with solid–fluid mixtures. The
Description of the instruments and apparatus
The experimental apparatus is composed of three main components: a viscometer, its electronic control module, and a heating jacket that heats the sensor (Fig. 1a and b). The viscometer, purchased from a commercial source (ViscoLab450 Viscometer from Cambridge Applied Systems, Massachusetts, USA), uses the so-called ‘piston-type’ technology (Fig. 1c) in which the determination of the fluid sample viscosity is based on the Couette flow inside a cylindrical chamber. It is composed of two magnetic
Nanofluids viscosities at ambient temperature
Fig. 3, Fig. 4 show viscosity data for Al2O3–water and CuO–water nanofluids for room condition (i.e., temperatures of fluid sample between 22 °C and 25 °C) and the particle volume fraction varying from 0.15% to as high as 13%. The results are expressed under the usual form of the viscosity as well as relative viscosity (defined as the ‘nanofluid-to-water’ ratio of viscosities). It is important to recall that based on the results from the validation tests using calibration fluid and distilled
Conclusion
In this paper, we have established a new and more complete viscosity data base for two particular water-based nanofluids, namely water–Al2O3 with 36 nm and 47 nm particle-sizes and water–CuO with 29 nm particle-size. Using a ‘piston-type’ calibrated viscometer combined with a cylindrical and heated chamber, data were first collected for ambient condition and particle volume fractions up to almost 12%. The effects due to temperature and particle-size were investigated for fluid temperatures ranging
Acknowledgements
The authors thank the Natural Sciences and Engineering Research Council of Canada and the Faculty of the Graduate Studies and Research of the Université de Moncton for financial support for the present project, and to the Institut Supérieur de Technologie Midi-Pyrénées and Pierre Fabre Medicament Production (France) for making possible an internship to Mr. Franck Desgranges. Our sincere thanks are also to Mrs. Vivienne Galanis for reading and improving the revised text.
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