ライブラリ登録: Guest
Nanoscience and Technology: An International Journal

年間 4 号発行

ISSN 印刷: 2572-4258

ISSN オンライン: 2572-4266

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.3 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.7 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.7 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00023 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.11 SJR: 0.244 SNIP: 0.521 CiteScore™:: 3.6 H-Index: 14

Indexed in

EFFECT OF AGGREGATION MORPHOLOGY ON THERMAL CONDUCTIVITY AND VISCOSITY OF Al2O3-CO2 NANOFLUID: A MOLECULAR DYNAMICS APPROACH

巻 12, 発行 1, 2021, pp. 19-37
DOI: 10.1615/NanoSciTechnolIntJ.2020033951
Get accessGet access

要約

Transport properties such as thermal conductivity and viscosity of carbon dioxide play an important role in rapidly evolving applications such as industrial refrigeration and enhanced recovery from oil wells. Although the addition of nanoparticles in CO2-based fluid has been known to enhance these transport properties, a detailed study of the effects of nanoparticle aggregation and its effects on transport properties is missing. In this work, we evaluate the potential energies associated with stable morphologies of Al2O3 nanoparticle aggregates in CO2. Using molecular dynamics simulations and the Green−Kubo formalism, we estimate the thermophysical properties of interest. Results indicate that the enhancement in the thermal conductivity and viscosity of nanofluid is inversely proportional to the system potential energy, and nanoparticle aggregation results in thermal conductivity enhancement by up to 70% and in viscosity enhancement by up to 84% at a volume fraction of about 0.9%. Results also indicate that different aggregation morphologies result in different potential energies; we expect the results from this paper to provide insights into particle aggregation morphologies and control.

参考
  1. Ahmed, Z., Bhargav, A., and Mallajosyula, S.S., Estimating Al2O3-CO2 Nanofluid Viscosity. A Molecular Dynamics Approach, Eur. Phys. J. Appl. Phys., vol. 84, p. 30902, 2018.

  2. Aimoli, C.G., Maginn, E.J., and Abreu, C.R., Transport Properties of Carbon Dioxide and Methane from Molecular Dynamics Simulations, J. Chem. Phys., vol. 141, p. 134101, 2014.

  3. Allen, M.P. and Tildesley, D.J., Computer Simulation of Liquids, Oxford, UK. Oxford University Press, 2017.

  4. Batchelor, G.K., The Effect of Brownian Motion on the Bulk Stress in a Suspension of Spherical Particles, J. Fluid Mech, vol. 83, pp. 97-117, 1977.

  5. Bernardo, P., Drioli, E., and Golemme, G., Membrane Gas Separation. A Review/State of the Art, Ind. Eng. Chem. Res., vol. 48, pp. 4638-4663, 2009.

  6. Bushehri, M.K., Mohebbi, A., and Rafsanjani, H.H., Prediction of Thermal Conductivity and Viscosity of Nanofluids by Molecular Dynamics Simulation, J. Eng. Thermophys., vol. 25, pp. 389-400, 2016.

  7. Darden, T., York, D., and Pedersen, L., Particle Mesh Ewald. An N log (N) Method for Ewald Sums in Large Systems, J. Chem. Phys, vol. 98, pp. 10089-10092, 1993.

  8. Dezfoli, A.R.A., Adab, Z., and Mehrabian, S., A Molecular Dynamic Simulation of the Behavior of Water Molecules inside a Carbon Nanotube, Nanosci. Technol. Int. J., vol. 1, no. 3, pp. 247-255, 2010.

  9. Duan, F., Kwek, D., and Crivoi, A., Viscosity Affected by Nanoparticle Aggregation in Al2O3-Water Nanofluids, Nanoscale Res. Lett., vol. 6, p. 248, 2011.

  10. Eastman, J.A., Phillpot, S.R., Choi, S.U.S., and Keblinski, P., Thermal Transport in Nanofluids, Annu. Rev. Mater. Res, vol. 34, pp. 219-246, 2004.

  11. Farzaneh, H., Behzadmehr, A., Yaghoubi, M., Samimi, A., and Sarvari, S.M.H., Stability of Nanofluids: Molecular Dynamic Approach and Experimental Study, Energy Convers. Manage., vol. 111, pp. 1-14, 2016.

  12. Feng, Y., Yu, B., Xu, P., and Zou, M., The Effective Thermal Conductivity of Nanofluids Based on the Nanolayer and the Aggregation of Nanoparticles, J. Phys. D Appl. Phys., vol. 40, 3164, 2007.

  13. Gaganpreet and Srivastava, S., Effect of Aggregation on Thermal Conductivity and Viscosity of Nanofluids, Appl. Nanosci., vol. 2, pp. 325-331, 2012.

  14. Garg, J., Poudel, B., Chiesa, M., Gordon, J.B., Ma, J.J., Wang, J.B., Ren, Z.F., Kang, Y.T., Ohtani, H., Nanda, J., and McKinley, G.H., Enhanced Thermal Conductivity and Viscosity of Copper Nanoparticles in Ethylene Glycol Nanofluid, J. Appl. Phys., vol. 103, p. 074301, 2008.

  15. Gullapalli, P., Tsau, J.S., and Heller, J.P., Gelling Behavior of 12-Hydroxystearic Acid in Organic Fluids and Dense CO2, SPE Int. Symp. on Oilfield Chemistry, SPE-28979-MS, February 14-17, San Antonio, Texas, Society of Petroleum Engineers, 1995.

  16. Harris, J.G. and Yung, K.H., Carbon Dioxide's Liquid-Vapor Coexistence Curve and Critical Properties as Predicted by a Simple Molecular Model, J. Phys. Chem., vol. 99, pp. 12021-12024, 1995.

  17. Huang, Z., Shi, C., Xu, J., Kilic, S., Enick, R.M., and Beckman, E.J., Enhancement of the Viscosity of Carbon Dioxide Using Styrene/Fluoroacrylate Copolymers, Macromolecules, vol. 33, pp. 5437-5442, 2000.

  18. Humphrey, W., Dalke, A., and Schulten, K., VMD: Visual Molecular Dynamics, J. Mol. Graph., vol. 14, pp. 33-38, 1996.

  19. Jabbari, F., Rajabpour, A., and Saedodin, S., Thermal Conductivity and Viscosity of Nanofluids: A Review of Recent Molecular Dynamics Studies, Chem. Eng. Sci., vol. 174, pp. 67-81, 2017.

  20. Kang, H., Zhang, Y., Yang, M., and Li, L., Molecular Dynamics Simulation on Effect of Nanoparticle Aggregation on Transport Properties of a Nanofluid, J. Nanotechnol. Eng. Med., vol. 3, no. 2, 021001, 2012.

  21. Keblinski, P., Phillpot, S.R., Choi, S.U.S., and Eastman, J.A., Mechanisms of Heat Flow in Suspensions of Nano-Sized Particles (Nanofluids), Int. J. Heat Mass Transf., vol. 45, pp. 855-863, 2002.

  22. Lee, S.L., Saidur, R., Sabri, M.F.M., and Min, T.K., Molecular Dynamic Simulation on the Thermal Conductivity of Nanofluids in Aggregated and Non-Aggregated States, Numer. Heat Transf. A-Appl, vol. 68, pp. 432-453, 2015.

  23. Lepilleur, C., Beckman, E.J., Schonemann, H., and Krukonis, V.J., Effect of Molecular Architecture on the Phase Behavior of Fluoroether-Functional Graft Copolymers in Supercritical CO2, Fluid Phase Equilibr., vol. 134, pp. 285-305, 1997.

  24. Li, L., Zhang, Y., Ma, H., and Yang, M., Molecular Dynamics Simulation of Effect of Liquid Layering around the Nanoparticle on the Enhanced Thermal Conductivity of Nanofluids, J. Nanoparticle Res, vol. 12, pp. 811-821, 2010.

  25. Ma, Q. and Fang, H., Viscosity Prediction of Water-Based Silver Nanofluid Using Equilibrium Molecular Dynamics, in ASME 2016 Int. Mechanical Engineering Congress and Exposition, IMECE2016-65831, 2016.

  26. Muraleedharan, M.G., Sundaram, D.S., Henry, A., and Yang, V., Thermal Conductivity Calculation of Nano-Suspensions Using Green-Kubo Relations with Reduced Artificial Correlations, J. Phys. Condens. Matter, vol. 29, 155302, 2017.

  27. Pastoriza-Gallego, M.J., Casanova, C., Paramo, R., Barbes, B., Legido, J.L., and Pineiro, M.M., A Study on Stability and Thermophysical Properties (Density and Viscosity) of Al2O3 in Water Nanofluid, J. Appl. Phys, vol. 106, 064301, 2009.

  28. Perera, M.S.A., Gamage, R.P., Rathnaweera, T.D., Ranathunga, A.S., Koay, A., and Choi, X., A Review of CO2-Enhanced Oil Recovery with a Simulated Sensitivity Analysis, Energies, vol. 9, 481, 2016.

  29. Plimpton, S., Fast Parallel Algorithms for Short-Range Molecular Dynamics, J. Comput. Phys., vol. 117, pp. 1-19, 1995.

  30. Rapaport, D.C., The Art of Molecular Dynamics Simulation, Cambridge, UK: Cambridge University Press, 2004.

  31. Sarkar, S. and Selvam, R.P., Molecular Dynamics Simulation of Effective Thermal Conductivity and Study of Enhanced Thermal Transport Mechanism in Nanofluids, J. Appl. Phys., vol. 102, 074302, 2007.

  32. Sedighi, M. and Mohebbi, A., Investigation of Nanoparticle Aggregation Effect on Thermal Properties of Nanofluid by a Combined Equilibrium and Non-Equilibrium Molecular Dynamics Simulation, J. Mol. Liq, vol. 197, pp. 14-22, 2014.

  33. Sharma, S., Chandra, R., Kumar Kushwaha, P., and Kumar, N., Molecular Dynamics Simulation of Carbon Nanotubes, Nanosci. Technol. Int. J., vol. 4, no. 1, pp. 1-27, 2013.

  34. Sertkaya, A.A., Determination of the Thermophysical Properties of a Zeolite Nanofluid, Heat Transf. Res., vol. 49, no. 7, pp. 583-596, 2018.

  35. Solemdal, Y., Eikevik, T.M., Tolstorebrov, I., and Veiby, O.J., CO2 as a Refrigerant for Cooling of Data-Center: A Case Study, Refrig. Sci. Technol. Proc., 2015.

  36. Tang, H., Liu, D., Zhao, Y., Yang, X., Lu, J., and Cui, F., Molecular Dynamics Study of the Aggregation Process of Graphene Oxide in Water, J. Phys. Chem. C, vol. 119, pp. 26712-26718, 2015.

  37. Vashishta, P., Kalia, R.K., Nakano, A., and Rino, J.P., Interaction Potentials for Alumina and Molecular Dynamics Simulations of Amorphous and Liquid Alumina, J. Appl. Phys., vol. 103, p. 083504, 2008.

  38. Wang, R., Qian, S., and Zhang, Z., Investigation of the Aggregation Morphology of Nanoparticle on the Thermal Conductivity of Nanofluid by Molecular Dynamics Simulations, Int. J. Heat Mass Transf, vol. 127, pp. 1138-1146, 2018.

  39. Xie, H., Chen, L., and Wu, Q., Measurements of the Viscosity of Suspensions (Nanofluids) Containing Nanosized Al2O3 Particles, High Temp. High Pressure, vol. 37, no. 2, pp. 127-135, 2008.

  40. Yang, H., Xu, Z., Fan, M., Gupta, R., Slimane, R.B., Bland, A.E., and Wright, I., Progress in Carbon Dioxide Separation and Capture: A Review, J. Environ. Sci., vol. 20, pp. 14-27, 2008.

  41. Zhong, H., Lai, S., Wang, J., Qiu, W., Ludemann, H.D., and Chen, L., Molecular Dynamics Simulation of Transport and Structural Properties of CO2 Using Different Molecular Models, J. Chem. Eng. Data, vol. 60, pp. 2188-2196, 2015.

によって引用された
  1. Zhang Xilong, Li Junhao, A review of uncertainties in the study of heat transfer properties of nanofluids, Heat and Mass Transfer, 2022. Crossref

Begell Digital Portal Begellデジタルライブラリー 電子書籍 ジャーナル 参考文献と会報 リサーチ集 価格及び購読のポリシー Begell House 連絡先 Language English 中文 Русский Português German French Spain