Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks

Powder Technology

Volumn 278, Issue , 2015, Pages 1-10

  Ariana, M A ^a   Vaferi, B ^b   Karimi, G ^c  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c SHIRAZ UNIVERSITY   (Iran)

Author keywords

Alumina water based nanofluids; Artificial neural network; Thermal conductivity ratio

Indexed keywords

ALUMINA; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; MEAN SQUARE ERROR; NANOFLUIDICS; NEURAL NETWORKS; THERMAL CONDUCTIVITY;

BACKPROPAGATION LEARNING ALGORITHM; HIDDEN NEURONS; PREDICTIVE CAPABILITIES; REGRESSION COEFFICIENT; RELATIVE DEVIATIONS; STATISTICAL ACCURACY; THERMAL CONDUCTIVITY RATIO; WATER BASED;

THERMAL CONDUCTIVITY OF LIQUIDS;

ALUMINUM OXIDE; NANOPARTICLE;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CELL COUNT; HIGH TEMPERATURE; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; NERVE CELL; PARTICLE SIZE; PERCEPTRON; PREDICTION; REPRODUCIBILITY; THERMAL CONDUCTIVITY; VALIDITY;

EID: 84925146611     PISSN: 00325910     EISSN: 1873328X     Source Type: Journal    
DOI: 10.1016/j.powtec.2015.03.005     Document Type: Article

References (45)

1. Maxwell J.C. A Treatise on Electricity and Magnetism 1873, Oxford University Press, London.
2. Lee S., Choi S.U.S., Li S., Eastman J.A. Measuring thermal conductivity of fluids containing oxide nanoparticles. J. Heat. Trans-T. ASME. 1999, 121:280-289.
3. Wang X., Xu X., Choi S.U.S. Thermal conductivity of nanoparticle-fluid mixture. J. Thermophys. Heat Transf. 1999, 13:474-480.
4. Xie H., Wang J., Xi T., Liu Y., Ai F., Wu Q. Thermal conductivity enhancement of suspensions containing nanosized alumina particles. J. Appl. Phys. 2002, 91:4568-4572.
5. Kahani M., Zeinali Heris S., Mousavi S.M. Comparative study between metal oxide nanopowders on thermal characteristics of nanofluid flow through helical coils. Powder Technol. 2013, 246:82-92.
6. Das S.K., Putra N., Thiesen P., Roetzel W. Temperature dependence of thermal conductivity enhancement for nanofluids. J. Heat. Trans-T. ASME. 2003, 125:567-574.
7. Yoo D.H., Hong K.S., Yang H.S. Study of thermal conductivity of nanofluids for the application of heat transfer fluids. Thermochim. Acta 2007, 455:66-69.
8. Hatami M., Sheikholeslami M., Ganji D.D. Laminar flow and heat transfer of nanofluid between contracting and rotating disks by least square method. Powder Technol. 2014, 253:769-779.
9. Hamilton R.L., Crosser O.K. Thermal conductivity of heterogeneous two component systems. Ind. Eng. Chem. Fundam. 1962, 1:187-191.
10. 2 ultra-fine particles. Netsu Bussei 1993, 7:227-233.
11. 2-water based nanofluids. Int. J. Therm. Sci. 2005, 44:367-373.
12. 3) thermal conductivity enhancement. Appl. Phys. Lett. 2005, 87:153107-1-153107-3.
13. Li C.H., Peterson G.P. Experimental investigation of temperature and volume fraction variations on the effective thermal conductivity of nanoparticle suspensions (nanofluids). J. Appl. Phys. 2006, 99:084314-1-084314-8.
14. Murshed S.M.S., Leong K.C., Yang C. Investigations of thermal conductivity and viscosity of nanofluids. Int. J. Therm. Sci. 2008, 47:560-568.
15. Teng T.P., Hung Y.H., Teng T.C., Mo H.E., Hsu H.G. The effect of alumina/water nanofluid particle size on thermal conductivity. Appl. Therm. Eng. 2010, 30:2213-2218.
16. Nan C.W., Birringer R., Clarke D.R., Gleiter H. Effective thermal conductivity of particulate composites with interfacial thermal resistance. J. Appl. Phys. 1997, 81:6692-6699.
17. Yu W., Choi S.U.S. The role of interfacial layers in the enhanced thermal conductivity of nanofluids: a renovated Maxwell model. J. Nanoparticle Res. 2003, 5:167-171.
18. Xie H., Fujii M., Zhang X. Effect of interfacial nanolayer on the effective thermal conductivity of nanoparticle-fluid mixture. Int. J. Heat Mass Transfer 2005, 48:2926-2932.
19. Xuan Y., Li Q., Hu W. Aggregation structure and thermal conductivity of nanofluids. AICHE J. 2003, 49:1038-1043.
20. Koo J., Kleinstreuer C. A new thermal conductivity model for nanofluids. J. Nanoparticle Res. 2004, 6:577-588.
21. Mintsa H.A., Roy G., Nguyen C.T., Doucet D. New temperature dependent thermal conductivity data for water-based nanofluids. Int. J. Therm. Sci. 2009, 48:363-371.
22. Bulsari A.B. Neural Networks for Chemical Engineers 1995, Elsevier, Amsterdam.
23. Vaferi B., Gitifar V., Darvishi P., Mowla D. Modeling and analysis of effective thermal conductivity of sandstone at high pressure and temperature using optimal artificial neural networks. J. Petrol. Sci. Eng. 2014, 119:69-78.
24. Sablani S.S., Baik O.D., Marcotte M. Neural networks for predicting thermal conductivity of bakery products. J. Food Eng. 2002, 52:299-304.
25. Hojjat M., Etemad S.G., Bagheri R., Thibault J. Thermal conductivity of non-Newtonian nanofluids: experimental data and modeling using neural network. Int. J. Heat Mass Transfer 2011, 54:1017-1023.
26. Papari M.M., Yousefi F., Moghadasi J., Karimi H., Campo A. Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks. Int. J. Therm. Sci. 2011, 50:44-52.
27. Longo G.A., Zilio C., Ceseracciu E., Reggiani M. Application of artificial neural network (ANN) for the prediction of thermal conductivity of oxide-water nanofluids. Nano Energy 2012, 1:290-296.
28. Malvandi A., Ganji D.D. Mixed convective heat transfer of water/alumina nanofluid inside a vertical microchannel. Powder Technol. 2014, 263:37-44.
29. 4Cp MMCs using artificial neural network. Arab. J. Sci. Eng. 2014, 39:6351-6361.
30. 4C composite powders in a planetary mill using an artificial neural network. Sci. Eng. Compos. Mater. 2014, 21:411-420.
31. Canakci A., Varol T., Ozsahin S. Analysis of the effect of a new process control agent technique on the mechanical milling process using a neural network model: measurement and modeling. Measurement 2013, 46:1818-1827.
32. Vaferi B., Samimi F., Pakgohar E., Mowla D. Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes. Powder Technol. 2014, 267:1-10.
33. Canakci A., Ozsahin S., Varol T. Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks. Powder Technol. 2012, 228:26-35.
34. Vaferi B., Eslamloueyan R., Ayatollahi S. Automatic recognition of oil reservoir models from well testing data by using multi-layer perceptron networks. J. Petrol. Sci. Eng. 2011, 77:254-262.
35. sC composites produced by powder metallurgy. Compos. Part B Eng. 2013, 54:224-233.
36. Marquardt D.W. An algorithm for least-squares estimation of nonlinear parameters. SIAM. J. 1963, 11:431-441.
37. Longo G.A., Zilio C. Experimental measurement of thermophysical properties of oxide-water nano-fluids down to ice-point. Exp. Thermal Fluid Sci. 2011, 35:1313-1324.
38. Timofeeva E.V., Gavrilov A.N., McCloskey J.M., Tolmachev Y.V., Sprunt S., Lopatina L.M., Selinger J.V. Thermal conductivity and particle agglomeration in alumina nanofluids: experiment and theory. Phys. Rev. E 2007, 76:061203-1-061203-16.
39. Zhang X., Gu H., Fujii M. Effective thermal conductivity and thermal diffusivity of nanofluids containing spherical and cylindrical nanoparticles. Exp. Thermal Fluid Sci. 2007, 31:593-599.
40. Yiamsawasd T., Dalkilic A.S., Wongwises S. Measurement of the thermal conductivity of titania and alumina nanofluids. Thermochim. Acta 2012, 545:48-56.
41. Buschmann M.H. Thermal conductivity and heat transfer of ceramic nanofluids. Int. J. Therm. Sci. 2012, 62:19-28.
42. Beck M., Yuan Y., Warrier P., Teja A. The effect of particle size on the thermal conductivity of alumina nanofluids. J. Nanoparticle Res. 2009, 11:1129-1136.
43. Beck M., Yuan Y., Warrier P., Teja A. The thermal conductivity of alumina nanofluids in water, ethylene glycol, and ethylene glycol+water mixtures. J. Nanoparticle Res. 2010, 12:1469-1477.
44. 3/water nanofluid. Exp. Thermal Fluid Sci. 2010, 34:210-216.
45. 3-water nanofluids. J. Appl. Phys. 2007, 101:044312-1-044312-5.