Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T16:22:01.647Z Has data issue: false hasContentIssue false

Thermal Conductivity Computation of Nanofluids by Equilibrium Molecular Dynamics Simulation: Nanoparticle Loading and Temperature Effect

Published online by Cambridge University Press:  01 February 2011

Suranjan Sarkar
Affiliation:
[email protected], University of Arkansas, Computational Mechanics and Nanotechnology Modeling laboratory, 735 W Treadwell St., Apt 37, Fayetteville, AR, 72701, United States, 2149577389
R. Panneer Selvam
Affiliation:
[email protected], University of Arkansas, Computational Mechanics Laboratory, BELL 4190, University of Arkansas, Fayetteville, AR, 72701, United States
Get access

Abstract

A model nanofluid system of copper nanoparticles in argon base fluid was successfully modeled by molecular dynamics simulation. The interatomic interactions between solid copper nanoparticles, base liquid argon atoms and between solid copper and liquid argon were modeled by Lennard Jones potential with appropriate parameters. The effective thermal conductivity of the nanofluids was calculated through Green Kubo method in equilibrium molecular dynamics simulation for varying nanoparticle concentrations and for varying system temperatures. Thermal conductivity of the basefluid was also calculated for comparison. This study showed that effective thermal conductivity of nanofluids is much higher than that of the base fluid and found to increase with increased nanoparticle concentration and system temperature. Through molecular dynamics calculation of mean square displacements for basefluid, nanofluid and its components, we suggested that the increased movement of liquid atoms in the presence of nanoparticle was probable mechanism for higher thermal conductivity of nanofluids.

Type
Research Article
Copyright
Copyright © Materials Research Society 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Lee, S., Choi, S. U. S., Lu, S., and Eastman, J. A., J. Heat Transfer 121, 280 (1999).Google Scholar
2. Eastman, J. A., Choi, S. U. S., and Thompson, L. J., Appl. Phys. Lett. 78, 718 (2001).Google Scholar
3. Choi, S. U. S., Zhang, Z. G., Yu, W., and Grulke, E. A., Appl. Phys. Lett. 79, 2252 (2001).Google Scholar
4. Das, S. K., Putra, N., Thiesen, P., and Roetzel, W., J. Heat Transfer 125, 567 (2003).Google Scholar
5. Hamilton, R. L., and Crosser, O. K., I&EC Fundamentals 1, 187 (1962).Google Scholar
6. Yu, W. and Choi, S. U. S., J. Nanosc. Nanotech. 5, 580, (2005).Google Scholar
7. Prasher, R., Bhattacharya, P. and Phelan, P. E., J. Heat Transfer 128, 588 (2006).Google Scholar
8. Kumar, D. H., Patel, H. E., Kumar, V. R. R., Sundararajan, T., Pradeep, T., and Das, S. K., Phys. Rev. Lett. 93, 14430 (2004).Google Scholar
9. Bhattacharya, P., Saha, S. K., Yadav, A., Phelan, P. E., and Prasher, R. S., J. Appl. Phys. 95, 6492 (2004).Google Scholar
10. McQuarrie, D. A., Statistical Mechanics (Harper & Row, New York, 1976).Google Scholar
11. Hoheisel, C., Theoretical Treatment of Liquids and Liquid Mixture (Elsevier, 1993).Google Scholar
12. Eapen, J., Li, J., and Yip, S., Phys. Rev. Lett. 98, 028302 (2007)Google Scholar
13. Schelling, P. K., Phillpot, S. R., and Keblinski, P., Phys. Rev. B 65, 144306 (2002).Google Scholar
14. Allen, M. P. and Tildesley, D. J., Computer Simulations of Liquids (Oxford, 1987).Google Scholar
15. Millett, P. C., Selvam, R P., Bansal, S., and Saxena, A., Acta Materialia 53, 3671 (2005).Google Scholar
16. Hoover, W. G., Phys. Rev. A 31, 1695 (1985).Google Scholar
17. Keblinski, P., Phillpot, S. R., Choi, S. U. S and Eastman, J. A., Int. J. Heat Mass Transfer 45, 855 (2002).Google Scholar
18. Prasher, R., Evans, W., Meakin, P., Fish, J., Phelan, P. E. and Keblinski, P., Appl. Phys. Lett. 89, 143119 (2006).Google Scholar