Heat transfer analysis of unsteady graphene oxide nanofluid flow using a fuzzy identifier evolved by genetically encoded mutable smart bee algorithm

Engineering Science and Technology, an International Journal

Volumn 18, Issue 1, 2015, Pages 106-123

  Azimi, Mohammadreza ^a   Mozaffari, Ahmad ^b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Fuzzy inference system; Graphene oxide; Heat transfer analysis; Hybrid genetic mutable smart bee algorithm; Nanofluid; Squeezing flow

Indexed keywords

EID: 85017355611     PISSN: None     EISSN: 22150986     Source Type: Journal    
DOI: 10.1016/j.jestch.2014.10.002     Document Type: Article

References (55)

1. [1] Ganji, D.D., Azimi, M., Application of DTM on MHD Jeffery Hamel flow with nanoparticle. U. P. B. Sci. Bull. 75:1 (2013), 223–230.
2. [2] Siddiqui, A.M., Irum, S., Ansari, A.R., Unsteady squeezing flow of a viscous MHD fluid between parallel plates. Math. Modell. Anal. 13 (2008), 565–576.
3. [3] Stefan, M.J., Versuch Über die scheinbare Adhäsion Sitzungsber. Abt. II Österr. Akad. Wiss. Math. Naturwiss. Kl. 69 (1874), 713–721.
4. [4] Leider, P.J., Bird, R.B., Squeezing flow between parallel disks, I: theoretical analysis. Ind. Eng. Chem. Fundam. 13 (1974), 336–341.
5. [5] Rashidi, M.M., Shahmohamadi, H., Dinarvand, S., Analytic approximate solutions for unsteady two-dimensional and axisymmetric squeezing flows between parallel plates. Math. Probl. Eng., 2008, 2008 Article ID 935095.
6. [6] Sherwood, J.D., Squeeze flow of a power-law fluid between non-parallel plates. J. Non-Newtonian Fluid Mech. 166 (2011), 289–296.
7. [7] Hamza, E.A., The magnetohydrodynamic effects on a fluid film squeezed between two rotating surfaces. J. Phys. Part D Appl. Phys. 24 (1991), 547–554.
8. [8] Hamza, E.A., Macdonald, D.A., A fluid film squeezed between two parallel plane surfaces. J. Fluid Mech. 109 (1981), 147–160.
9. [9] Singh, P., Radhakrishnan, V., Narayan, K.A., Squeezing flow between parallel plates. Ing. Arch. 60 (1990), 274–281.
10. [10] Islam, S., Khan, H., Shah, I.A., Zaman, G., An axisymmetric squeezing fluid flow between the two infinite parallel plates in a porous medium channel. Math. Probl. Eng., 2011, 2011 Article ID 349803.
11. [11] Munawar, S., Mehmood, A., Ali, A., Three-dimensional squeezing flow in a rotating channel of lower stretching porous wall. Comput. Math. Appl. 64 (2012), 1575–1586.
12. [12] Choi, S.U.S., Eastman, J.A., Enhancing thermal conductivity of fluids with nanoparticles. Mater. Sci. 231 (1995), 99–105.
13. [13] Das, S.K., Temperature dependence of thermal conductivity enhancement for nanofluids. ASME J. Heat Transfer 125 (2003), 567–574.
14. [14] Mohammadian, S.K., Seyf, H.R., Zhang, Y., Performance augmentation and optimization of aluminum oxide–water nanofluid flow in a two-fluid microchannel heat exchanger. ASME J. Heat Transfer, 136(2), 2013, 021701.
15. [15] Kleinstreuer, C., Feng, Y., Thermal nanofluid property model with application to nanofluid flow in a parallel-disk system-part I: a new thermal conductivity model for nanofluid flow. ASME J. Heat Transfer, 134(5), 2012, 051002.
16. [16] Azimi, M., Azimi, A., Mirzaei, M., Investigation of the unsteady graphene oxide nanofluid flow between two moving plates. J. Comput. Theor. Nanosci. 11:10 (2014), 1–5.
17. [17] Mozaffari, A., Azimi, M., Gorji-Bandpy, M., Ensemble mutable smart bee algorithm and a robust neural identifier for optimal design of a large scale power system. J. Comput. Sci. 5:2 (2014), 206–223.
18. [18] Mozaffari, A., Gorji-Bandpy, M., Samadian, P., Rastgar, R., Rezania, A., Comprehensive preference optimization of an irreversible thermal engine using Pareto based mutable smart bee algorithm and generalized regression neural network. Swarm Evol. Comput. 9 (2013), 90–103.
19. [19] Mozaffari, A., Fathi, A., Khajepour, A., Toyserkani, E., Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using novel intelligent evolutionary algorithms. Appl. Soft Comput. 13:3 (2013), 1505–1519.
20. [20] Mozaffari, A., Behzadipour, S., Kohani, M., Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisor. Appl. Soft Comput. 14 (2014), 12–30.
21. [21] Sheikholeslami, M., Bani-Sheykholeslami, F., Khoshhal, S., Mola-Abasia, H., Ganji, D.D., Rokni, H.B., Effect of magnetic field on Cu–water nanofluid heat transfer using GMDH-type neural network. Neural Comput. Appl., 2013, 10.1007/s00521-013-1459-y.
22. [22] Fathi, A., Mozaffari, A., Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self organizing map. J. Intell. Manuf., 2012, 10.1007/s10845-012-0718-6.
23. [23] Karaboga, D., Basturk, B., On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8 (2007), 687–697.
24. [24] An, J., Kang, Q., Wang, L., Wu, Q., Mussels wandering optimization: an ecologically inspired algorithm for global optimization. Cognit. Comput. 5 (2013), 188–199.
25. [25] Mozaffari, A., Gorji-Bandpy, M., Gorji, T., Optimal design of constraint engineering systems: application of mutable smart bee algorithm. Int. J. Bio-Inspired Comput. 4:3 (2012), 167–180.
26. [26] Masuda, H., Ebata, A., Teramea, K., Hishinuma, N., Altering the thermal conductivity and viscosity of liquid by dispersing ultra-fine particles. Netsu Bussei 4 (1993), 227–233.
27. [27] Mozaffari, A., Ramiar, A., Fathi, A., Optimal design of classic Atkinson engine with dynamic specific heat using adaptive neuro-fuzzy inference system and mutable smart bee algorithm. Swarm Evol. Comput. 12 (2013), 74–91.
28. [28] Mozaffari, A., Azad, N.L., Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification. Neurocomputing 131 (2014), 143–156.
29. [29] Azimi, M., Ommi, F., Using nanofluid for heat transfer enhancement in engine cooling process. J. Nano Energy Power Res., 2, 2013, 2013.
30. [30] Sheikholeslami, M., Ganji, D.D., Heat transfer of Cu–water nanofluid flow between parallel plates. Powder Technol. 235 (2013), 873–879.
31. [31] Malvandi, A., Hedayati, F., Ganji, D.D., Slip effects on unsteady stagnation point flow of a nanofluid over a stretching sheet. Powder Technol. 253 (2014), 377–384.
32. [32] Sheikholeslami, M., Hatami, M., Ganji, D.D., Nanofluid flow and heat transfer in a rotating system in the presence of magnetic field. J. Mol. Liq. 190 (2014), 112–120.
33. [33] Malvandi, A., Ganji, D.D., Effects of nanoparticle migration on force convection of alumina/water nanofluid in a cooled parallel-plate channel. Adv. Powder Technol. 25 (2014), 1369–1375.
34. [34] Bahiraei, M., Hangi, M., Investigating the efficacy of magnetic nanofluid as a coolant in double-pipe heat exchanger in the presence of magnetic field. Energy Convers. Manag. 76 (2013), 1125–1133.
35. [35] Bahiraei, M., A comprehensive review on different numerical approaches for simulation in nanofluids: traditional and novel techniques. J. Dispersion Sci. Technol. 35 (2014), 984–996.
36. 2 non-Newtonian nanofluid passing through the porous media between two coaxial cylinders. J. Mol. Liq. 188 (2013), 155–161.
37. 3–water nanofluid flow over a horizontal plate. J. Mol. Liq. 187 (2013), 294–301.
38. [38] Hatami, M., Ganji, D.D., Heat transfer and nanofluid flow in suction and blowing process between parallel disks in presence of variable magnetic field. J. Mol. Liq. 190 (2014), 159–168.
39. [39] Hatami, M., Hatami, J., Ganji, D.D., Computer simulation of MHD blood conveying gold nanoparticles as a third grade non-Newtonian nanofluid in a hollow porous vessel. Comp. Methods Programs Biomed. 113 (2014), 632–641.
40. [40] Domairry, G., Hatami, M., Squeezing Cu–water nanofluid flow analysis between parallel plates by DTM-Padé method. J. Mol. Liq. 193 (2014), 37–44.
41. [41] Hatami, M., Ganji, D.D., Natural convection of sodium alginate (SA) non-Newtonian nanofluid flow between two vertical flat plates by analytical and numerical methods. Case Stud. Therm. Eng. 2 (2014), 14–22.
42. [42] Ahmadi, A.R., Zahmatkesh, A., Hatami, M., Ganji, D.D., A comprehensive analysis of the flow and heat transfer for a nanofluid over an unsteady stretching flat plate. Powder Technol. 258 (2014), 125–133.
43. [43] Hatami, M., Ganji, D.D., Thermal and flow analysis of microchannel heat sink (MCHS) cooled by Cu–water nanofluid using porous media approach and least square method. Energy Convers. Manag. 78 (2014), 347–358.
44. [44] Sheikholeslami, M., Gorji-Bandpy, M., Ellahi, R., Zeeshan, A., Simulation of MHD CuO–water nanofluid flow and convective heat transfer considering Lorentz forces. J. Magn. Magn. Mater. 369 (2014), 69–80.
45. [45] Sheikholeslami, M., Gorji-Bandpy, M., Ellahi, R., Hassan, M., Soleimani, S., Effects of MHD on Cu–water nanofluid flow and heat transfer by means of CVFEM. J. Magn. Magn. Mater. 349 (2014), 188–200.
46. [46] Ellahi, R., Aziz, S., Zeeshan, A., Non-Newtonian nanofluid flow through a porous medium between two coaxial cylinders with heat transfer and variable viscosity. J. Porous Media 16 (2013), 205–216.
47. [47] Ellahi, R., The effects of MHD and temperature dependent viscosity on the flow of non-Newtonian nanofluid in a pipe: analytical solutions. Appl. Math. Model. 37 (2013), 1451–1467.
48. [48] Ellahi, R., Raza, M., Vafai, K., Series solutions of non-Newtonian nanofluids with Reynolds’ model and Vogel's model by means of the homotopy analysis method. Math. Comp. Model. 55 (2012), 1876–1891.
49. [49] Sheikholeslami, M., Ellahi, R., Ashorynejad, H.R., Domairry, G., Hayat, T., Effects of heat transfer in flow of nanofluids over a permeable stretching wall in a porous medium. J. Comput. Theor. Nanosci. 11 (2014), 486–496.
50. [50] Zeeshan, A., Biag, M., Ellahi, R., Hayat, T., Flow of viscous nanofluid between the concentric cylinder. J. Comput. Theor. Nanosci. 11 (2014), 646–654.
51. [51] Ellahi, R., Hamed, M., Numerical analysis of steady non-Newtonian flows with heat transfer analysis, MHD and nonlinear slip effects. Int. J. Numer. Methods Heat Fluid Flow 22 (2012), 24–38.
52. [52] Tavman, I., Turgut, A., Chirtoc, M., Schuchmann, H.P., Tavman, S., Experimental investigation of viscosity and thermal conductivity of suspensions containing nanosized ceramic particles. Arch. Mater. Sci. Eng. 34 (2008), 99–104.
53. [53] Corcoine, M., Empirical correlating equations for predicting the effective thermal conductivity and dynamic viscosity of nanofluids. Energy Convers. Manag. 52 (2011), 789–793.
54. [54] Soleimani, S., Sheikholeslami, M., Ganji, D.D., Gorji-Bandpy, M., Natural convection heat transfer in a nanofluid filled semi-anulus enclosure. Int. Commun. Heat Mass Transfer 39 (2012), 565–574.
55. [55] Wang, X.Q., Mujumdar, A.S., Heat transfer characteristics of nanofluids: a review. Int. J. Therm. Sci. 46 (2007), 1–19.