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Effect of complex turbulator on heat transfer of nanomaterial considering turbulent flow

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To exhibit the nanomaterial hydrothermal behavior within a duct, FVM has been utilized and to augment the performance, helical complex device was incorporated. To achieve the formulation of problem, K–ɛ model was applied with considering CuO–water nanomaterial. Outputs in forms of velocity and temperature contours have been extracted. Stronger tangential contact of carrier fluid with outer wall guarantees the thinner boundary layer with rise of inlet velocity and friction loss augments. The increase of turbulator width results in augment of Nu owing to greater tangential flow and more fluctuations.

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b :

Width of turbulator

p :


\( f \) :

Friction factor

\( \text{Re} \) :

Reynolds number

\( T \) :

Fluid temperature

Pr :



Finite volume method

Nu :

Nusselt number

ρ :


\( \phi \) :

Fraction of nanomaterial

\( \mu_{t} \) :

Turbulent viscosity

\( \alpha \) :

Thermal diffusivity

μ :


f :




\( nf \) :

Carrier fluid


  1. Anbumeenakshi C, Thansekhar MR (2017) On the effectiveness of a nanofluid cooled microchannel heat sink under non-uniform heating condition. Appl Therm Eng 113:1437–1443

  2. Eldabe NTM, Abo-Seida OM, Abo Seliem AAS, Elshekhipy AA, Hegazy N (2018) Magnetohydrodynamic peristaltic flow of Williamson nanofluid with heat and mass transfer through a non-Darcy porous medium. Microsyst Technol 24:3751–3776

  3. Farshad SA, Sheikholeslami M (2019) Nanofluid flow inside a solar collector utilizing twisted tape considering exergy and entropy analysis. Renew Energy 141:246–258

  4. Farshad SA, Sheikholeslami M, Hosseini SH, Shafee A, Li Z (2019) Nanofluid turbulent forced convection through a solar flat plate collector with Al2O3 nanoparticles. Microsyst Technol. https://doi.org/10.1007/s00542-019-04430-2

  5. Gao W, Farahani MR (2017) Generalization bounds and uniform bounds for multi-dividing ontology algorithms with convex ontology loss function. Comput J 60(9):1289–1299

  6. Gao W, Wang WF (2015) Toughness and fractional critical deleted graph. Utilitas Math 98:295–310

  7. Gao W, Wang WF (2016) The eccentric connectivity polynomial of two classes of nanotubes. Chaos Solitons Fract 89:290–294

  8. Gao W, Wang WF (2018) Analysis of k-partite ranking algorithm in area under the receiver operating characteristic curve criterion. Int J Comput Math 95(8):1527–1547

  9. Gao W, Zhu LL (2014) Gradient learning algorithms for ontology computing. Comput Intell Neurosci. https://doi.org/10.1155/2014/438291(article ID 438291, 12 pages)

  10. Gao W, Liang L, Xu TW, Zhou JX (2016a) Degree conditions for fractional (g, f, n′, m)-critical deleted graphs and fractional ID-(g, f, m)-deleted graphs. Bull Malays Math Sci Soc 39:315–330

  11. Gao W, Farahani MR, Shi L (2016b) The forgotten topological index of some drug structures. Acta Med Mediterr 32:579–585

  12. Gao W, Siddiqui MK, Imran M, Jamil MK, Farahani MR (2016c) Forgotten topological index of chemical structure in drugs. Saudi Pharm J 24(3):258–264

  13. Gao W, Yan L, Shi L (2017a) Generalized Zagreb index of polyomino chains and nanotubes. Optoelectron Adv Mater Rapid Commun 11(1–2):119–124

  14. Gao W, Liang L, Xu TW, Gan JH (2017b) Topics on data transmission problem in software definition network. Open Phys 15:501–508

  15. Hedayat M, Sheikholeslami M, Shafee A, Nguyen-Thoi T, Henda MB, Tlili I, Li Z (2019) Investigation of nanofluid conduction heat transfer within a triplex tube considering solidification. J Mol Liq 290:111232

  16. Kim D, Kwon Y, Cho Y, Li C, Cheong S, Hwang Y, Lee J, Hong D, Moona S (2009) Convective heat transfer characteristics of nanofluids under laminar and turbulent flow conditions. Curr Appl Phys 9:119–123

  17. Kumar N, Sonawane SS (2016) Experimental study of Fe2O3/water and Fe2O3/ethylene glycol nanofluid heat transfer enhancement in a shell and tube heat exchanger. Int Commun Heat Mass Transfer 78:277–284

  18. Manca O, Nardini S, Ricci D (2012) A numerical study of nanofluid forced convection in ribbed channels. Appl Therm Eng 37:280–292

  19. Mwesigye A, Huan Z, Meyer JP (2015) Thermodynamic optimisation of the performance of a parabolic trough receiver using synthetic oil–Al2O3 nanofluid. Appl Energy 156:398–412

  20. Qin Y (2015a) A review on the development of cool pavements to mitigate urban heat island effect. Renew Sustain Energy Rev 52:445–459

  21. Qin Y (2015b) Urban canyon albedo and its implication on the use of reflective cool pavements. Energy Build 96:86–94

  22. Qin Y (2016) Pavement surface maximum temperature increases linearly with solar absorption and reciprocal thermal inertial. Int J Heat Mass Transf 97:391–399

  23. Qin Y, He H (2017) A new simplified method for measuring the albedo of limited extent targets. Sol Energy 157(Supplement C):1047–1055

  24. Qin Y, Hiller JE (2014) Understanding pavement-surface energy balance and its implications on cool pavement development. Energy Build 85:389–399

  25. Qin Y, Liang J, Tan K, Li F (2016a) A side by side comparison of the cooling effect of building blocks with retro-reflective and diffuse-reflective walls. Sol Energy 133:172–179

  26. Qin Y, Liang J, Yang H, Deng Z (2016b) Gas permeability of pervious concrete and its implications on the application of pervious pavements. Measurement 78:104–110

  27. Qin Y, Zhang M, Hiller JE (2017a) Theoretical and experimental studies on the daily accumulative heat gain from cool roofs. Energy 129:138–147

  28. Qin Y, He Y, Wu B, Ma S, Zhang X (2017b) Regulating top albedo and bottom emissivity of concrete roof tiles for reducing building heat gains. Energy Build 156(Supplement C):218–224

  29. Qin Y, Luo J, Chen Z, Mei G, Yan L-E (2018a) Measuring the albedo of limited-extent targets without the aid of known-albedo masks. Sol Energy 171:971–976

  30. Qin Y, He Y, Hiller JE, Mei G (2018b) A new water-retaining paver block for reducing runoff and cooling pavement. J Clean Prod 199:948–956

  31. Qin Y, Zhang M, Mei G (2018c) A new simplified method for measuring the permeability characteristics of highly porous media. J Hydrol 562:725–732

  32. Qin Y, Hiller JE, Meng D (2019a) Linearity between pavement thermophysical properties and surface temperatures. J Mater Civ Eng. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002890

  33. Qin Y, Zhao Y, Chen X, Wang L, Li F, Bao T (2019b) Moist curing increases the solar reflectance of concrete. Constr Build Mater 215:114–118

  34. Rafatijo H, Thompson DL (2017) General application of Tolman’s concept of activation energy. J Chem Phys 147:224111. https://doi.org/10.1063/1.5009751

  35. Rafatijo H, Monge-Palacios M, Thompson DL (2019) Identifying collisions of various molecularities in molecular dynamics simulations. J Phys Chem A 123(6):1131–1139. https://doi.org/10.1021/acs.jpca.8b11686

  36. Rao Y, Shao Z, Ahangarnejad AH, Gholamalizadeh E, Sobhani B (2019) Shark smell optimizer applied to identify the optimal parameters of the proton exchange membrane fuel cell model. Energy Convers Manag 182:1–8

  37. Seyednezhad M, Sheikholeslami M, Ali JA, Shafee A, Nguyen TK (2019) Nanoparticles for water desalination in solar heat exchanger; review. J Therm Anal Calorim. https://doi.org/10.1007/s10973-019-08634-6

  38. Shah Z, Islam S, Ayaz H, Khan S (2019) Radiative heat and mass transfer analysis of micropolar nanofluid flow of Casson fluid between two rotating parallel plates with effects of hall current. ASME J Heat Transf. https://doi.org/10.1115/1.4040415

  39. Shao Z, Wakil K, Usak M, Heidari MA, Wang B, Simoes R (2018) Kriging empirical mode decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid. Appl Therm Eng 145:58–70

  40. Shao Z, Ahangarnejad AH, Monazzah A, Rao Y, Rodriguez D (2019a) Increasing of fuel cell economic benefits by optimal participation strategy with energy storages and other distributed resources and considering uncertainties and various markets. Int J Hydrogen Energy 44:1839–1850

  41. Shao Z, Armaghani DJ, Bejarbaneh BY, Mu’azu MA, Mohamad ET (2019b) Estimating the friction angle of black shale core specimens with hybrid-ANN approaches. Measurement 145(2019):744–755

  42. Sheikholeslami M (2017) Magnetic field influence on CuO–H2O nanofluid convective flow in a permeable cavity considering various shapes for nanoparticles. Int J Hydrogen Energy 42:19611–19621

  43. Sheikholeslami M (2018a) Application of Darcy law for nanofluid flow in a porous cavity under the impact of Lorentz forces. J Mol Liq 266:495–503

  44. Sheikholeslami M (2018b) Finite element method for PCM solidification in existence of CuO nanoparticles. J Mol Liq 265:347–355

  45. Sheikholeslami M (2018c) Influence of magnetic field on Al2O3–H2O nanofluid forced convection heat transfer in a porous lid driven cavity with hot sphere obstacle by means of LBM. J Mol Liq 263:472–488

  46. Sheikholeslami M (2018d) Numerical simulation for solidification in a LHTESS by means of nano-enhanced PCM. J Taiwan Inst Chem Eng 86:25–41

  47. Sheikholeslami M (2018e) Numerical investigation of nanofluid free convection under the influence of electric field in a porous enclosure. J Mol Liq 249:1212–1221

  48. Sheikholeslami M (2019a) New computational approach for exergy and entropy analysis of nanofluid under the impact of Lorentz force through a porous media. Comput Methods Appl Mech Eng 344:319–333

  49. Sheikholeslami M (2019b) Numerical approach for MHD Al2O3–water nanofluid transportation inside a permeable medium using innovative computer method. Comput Methods Appl Mech Eng 344:306–318

  50. Sheikholeslami M, Ghasemi A (2018) Solidification heat transfer of nanofluid in existence of thermal radiation by means of FEM. Int J Heat Mass Transf 123:418–431

  51. Sheikholeslami M, Rokni HB (2017) Melting heat transfer influence on nanofluid flow inside a cavity in existence of magnetic field. Int J Heat Mass Transf 114:517–526

  52. Sheikholeslami M, Rokni HB (2018) Magnetic nanofluid flow and convective heat transfer in a porous cavity considering Brownian motion effects. Phys Fluids. https://doi.org/10.1063/1.5012517

  53. Sheikholeslami M, Shehzad SA (2017) CVFEM for influence of external magnetic source on Fe3O4–H2O nanofluid behavior in a permeable cavity considering shape effect. Int J Heat Mass Transf 115:180–191

  54. Sheikholeslami M, Shehzad SA (2018) CVFEM simulation for nanofluid migration in a porous medium using Darcy model. Int J Heat Mass Transf 122:1264–1271

  55. Sheikholeslami M, Vajravelu K (2017) Nanofluid flow and heat transfer in a cavity with variable magnetic field. Appl Math Comput 298:272–282

  56. Sheikholeslami M, Zeeshan A (2017) Analysis of flow and heat transfer in water based nanofluid due to magnetic field in a porous enclosure with constant heat flux using CVFEM. Comput Methods Appl Mech Eng 320:68–81

  57. Sheikholeslami M, Darzi M, Sadoughi MK (2018a) Heat transfer improvement and pressure drop during condensation of refrigerant-based nanofluid; an experimental procedure. Int J Heat Mass Transf 122:643–650

  58. Sheikholeslami M, Shehzad SA, Li Z, Shafee A, Abbasi FM (2018b) Time dependent conduction heat transfer during solidification in a storage system using nanoparticles. Microsyst Technol. https://doi.org/10.1007/s00542-018-4050-8

  59. Sheikholeslami M, Jafaryar M, Li Z (2018c) Nanofluid turbulent convective flow in a circular duct with helical turbulators considering CuO nanoparticles. Int J Heat Mass Transf 124:980–989

  60. Sheikholeslami M, Jafaryar M, Shafee A, Li Z (2019a) Analyze of entropy generation for NEPCM melting process inside a heat storage system. Microsyst Technol. https://doi.org/10.1007/s00542-019-04301-w

  61. Sheikholeslami M, Rezaeianjouybari B, Darzi M, Shafee A, Li Z, Nguyen TK (2019b) Application of nano-refrigerant for boiling heat transfer enhancement employing an experimental study. Int J Heat Mass Transf 141:974–980

  62. Sheikholeslami M, Zareei A, Jafaryar M, Shafee A, Li Z, Smida A, Tlili I (2019c) Heat transfer simulation during charging of nanoparticle enhanced PCM within a channel. Phys A Stat Mech Appl 525:557–565

  63. Sheikholeslami M, Haq R, Shafee A, Li Z, Elaraki YG, Tlili I (2019d) Heat transfer simulation of heat storage unit with nanoparticles and fins through a heat exchanger. Int J Heat Mass Transf 135:470–478

  64. Sheikholeslami M, Jafaryar M, Ali JA, Hamad SM, Divsalar A, Shafee A, Nguyen-Thoi T, Li Z (2019e) Simulation of turbulent flow of nanofluid due to existence of new effective turbulator involving entropy generation. J Mol Liq 291:111283

  65. Sheikholeslami M, Shafee A, Zareei A, Haq R, Li Z (2019f) Heat transfer of magnetic nanoparticles through porous media including exergy analysis. J Mol Liq 279:719–732

  66. Sheikholeslami M, Jafaryar M, Shafee A, Li Z (2019g) Nanofluid heat transfer and entropy generation through a heat exchanger considering a new turbulator and CuO nanoparticles. J Therm Anal Calorim. https://doi.org/10.1007/s10973-018-7866-7

  67. Sheikholeslami M, Gerdroodbary MB, Moradi R, Shafee A, Li Z (2019h) Application of neural network for estimation of heat transfer treatment of Al2O3–H2O nanofluid through a channel. Comput Methods Appl Mech Eng 344:1–12

  68. Sheikholeslami M, Jafaryar M, Hedayat M, Shafee A, Li Z, Nguyen TK, Bakouri M (2019i) Heat transfer and turbulent simulation of nanomaterial due to compound turbulator including irreversibility analysis. Int J Heat Mass Transf 137:1290–1300

  69. Sheikholeslami M, Haq R, Shafee A, Li Z (2019j) Heat transfer behavior of nanoparticle enhanced PCM solidification through an enclosure with V shaped fins. Int J Heat Mass Transf 130:1322–1342

  70. Sheikholeslami M, Jafaryar M, Shafee A, Li Z, Haq R (2019k) Heat transfer of nanoparticles employing innovative turbulator considering entropy generation. Int J Heat Mass Transf 136:1233–1240

  71. Sheremet MA, Cimpean DS, Pop I (2017) Free convection in a partially heated wavy porous cavity filled with a nanofluid under the effects of Brownian diffusion and thermophoresis. Appl Therm Eng 113:413–418

  72. Shi X, Lu W, Wang Z, Pan L, Cui G, Xu J, LaBean TH (2014) Programmable DNA tile self-assembly using a hierarchical sub-tile strategy. Nanotechnology 25(7):075602

  73. Yadav D, Bhargava R, Agrawal GS, Yadav N, Lee J, Kim MC (2014) Linear and nonlinear analysis of thermal instability in a rotating porous layer saturated by a non-Newtonian nanofluid with thermal conductivity and viscosity variation. Microfluid Nanofluid 16:425–440

  74. Zheng L, Xie Y, Zhang D (2017) Numerical investigation on heat transfer performance and flow characteristics in circular tubes with dimpled twisted tapes using Al2O3–water nanofluid. Int J Heat Mass Transf 111:962–981

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Correspondence to Iskander Tlili.

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Chen, L., Jafaryar, M., Shafee, A. et al. Effect of complex turbulator on heat transfer of nanomaterial considering turbulent flow. Microsyst Technol 26, 739–749 (2020). https://doi.org/10.1007/s00542-019-04617-7

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