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Experimental and theoretical studies of nanofluid thermal conductivity enhancement: a review.

Kleinstreuer C, Feng Y - Nanoscale Res Lett (2011)

Bottom Line: Such outcomes would validate new, minimally intrusive techniques and verify the reproducibility of experimental results.Dynamic knf models, assuming non-interacting metallic nano-spheres, postulate an enhancement above the classical Maxwell theory and thereby provide potentially additional physical insight.Clearly, it will be necessary to consider not only one possible mechanism but combine several mechanisms and compare predictive results to new benchmark experimental data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC 27695-7910, USA. ck@eos.ncsu.edu.

ABSTRACT
Nanofluids, i.e., well-dispersed (metallic) nanoparticles at low- volume fractions in liquids, may enhance the mixture's thermal conductivity, knf, over the base-fluid values. Thus, they are potentially useful for advanced cooling of micro-systems. Focusing mainly on dilute suspensions of well-dispersed spherical nanoparticles in water or ethylene glycol, recent experimental observations, associated measurement techniques, and new theories as well as useful correlations have been reviewed.It is evident that key questions still linger concerning the best nanoparticle-and-liquid pairing and conditioning, reliable measurements of achievable knf values, and easy-to-use, physically sound computer models which fully describe the particle dynamics and heat transfer of nanofluids. At present, experimental data and measurement methods are lacking consistency. In fact, debates on whether the anomalous enhancement is real or not endure, as well as discussions on what are repeatable correlations between knf and temperature, nanoparticle size/shape, and aggregation state. Clearly, benchmark experiments are needed, using the same nanofluids subject to different measurement methods. Such outcomes would validate new, minimally intrusive techniques and verify the reproducibility of experimental results. Dynamic knf models, assuming non-interacting metallic nano-spheres, postulate an enhancement above the classical Maxwell theory and thereby provide potentially additional physical insight. Clearly, it will be necessary to consider not only one possible mechanism but combine several mechanisms and compare predictive results to new benchmark experimental data sets.

No MeSH data available.


Related in: MedlinePlus

Comparison of experimental data. (a) Comparison of the experimental data for CuO-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80]. (b) Comparison of the experimental data for Al2O3-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80].
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Figure 3: Comparison of experimental data. (a) Comparison of the experimental data for CuO-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80]. (b) Comparison of the experimental data for Al2O3-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80].

Mentions: where D is the nanoparticle diffusion coefficient, κBoltzmann = 1.3807e-23 J/K is the Boltzmann constant, is the root mean square velocity of particles and λbf is the base fluid molecular mean free path. The definition of (see Eq. 7b) is different from Jang and Choi's 2006 model [79]. The arbitrary definitions of the coefficient "random motion velocity" brought questions about the model's generality [78]. Considering the model by Jang and Choi [78], Kleinstreuer and Li [80] examined thermal conductivities of nanofluids subject to different definitions of "random motion velocity". The results heavily deviated from benchmark experimental data (see Figure 3a,b), because there is no accepted way for calculating the random motion velocity. Clearly, such a rather arbitrary parameter is not physically sound, leading to questions about the model's generality [80].


Experimental and theoretical studies of nanofluid thermal conductivity enhancement: a review.

Kleinstreuer C, Feng Y - Nanoscale Res Lett (2011)

Comparison of experimental data. (a) Comparison of the experimental data for CuO-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80]. (b) Comparison of the experimental data for Al2O3-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3211287&req=5

Figure 3: Comparison of experimental data. (a) Comparison of the experimental data for CuO-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80]. (b) Comparison of the experimental data for Al2O3-water nanofluids with Jang and Choi's model [78] for different random motion velocity definitions [80].
Mentions: where D is the nanoparticle diffusion coefficient, κBoltzmann = 1.3807e-23 J/K is the Boltzmann constant, is the root mean square velocity of particles and λbf is the base fluid molecular mean free path. The definition of (see Eq. 7b) is different from Jang and Choi's 2006 model [79]. The arbitrary definitions of the coefficient "random motion velocity" brought questions about the model's generality [78]. Considering the model by Jang and Choi [78], Kleinstreuer and Li [80] examined thermal conductivities of nanofluids subject to different definitions of "random motion velocity". The results heavily deviated from benchmark experimental data (see Figure 3a,b), because there is no accepted way for calculating the random motion velocity. Clearly, such a rather arbitrary parameter is not physically sound, leading to questions about the model's generality [80].

Bottom Line: Such outcomes would validate new, minimally intrusive techniques and verify the reproducibility of experimental results.Dynamic knf models, assuming non-interacting metallic nano-spheres, postulate an enhancement above the classical Maxwell theory and thereby provide potentially additional physical insight.Clearly, it will be necessary to consider not only one possible mechanism but combine several mechanisms and compare predictive results to new benchmark experimental data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC 27695-7910, USA. ck@eos.ncsu.edu.

ABSTRACT
Nanofluids, i.e., well-dispersed (metallic) nanoparticles at low- volume fractions in liquids, may enhance the mixture's thermal conductivity, knf, over the base-fluid values. Thus, they are potentially useful for advanced cooling of micro-systems. Focusing mainly on dilute suspensions of well-dispersed spherical nanoparticles in water or ethylene glycol, recent experimental observations, associated measurement techniques, and new theories as well as useful correlations have been reviewed.It is evident that key questions still linger concerning the best nanoparticle-and-liquid pairing and conditioning, reliable measurements of achievable knf values, and easy-to-use, physically sound computer models which fully describe the particle dynamics and heat transfer of nanofluids. At present, experimental data and measurement methods are lacking consistency. In fact, debates on whether the anomalous enhancement is real or not endure, as well as discussions on what are repeatable correlations between knf and temperature, nanoparticle size/shape, and aggregation state. Clearly, benchmark experiments are needed, using the same nanofluids subject to different measurement methods. Such outcomes would validate new, minimally intrusive techniques and verify the reproducibility of experimental results. Dynamic knf models, assuming non-interacting metallic nano-spheres, postulate an enhancement above the classical Maxwell theory and thereby provide potentially additional physical insight. Clearly, it will be necessary to consider not only one possible mechanism but combine several mechanisms and compare predictive results to new benchmark experimental data sets.

No MeSH data available.


Related in: MedlinePlus