Skip to main content

Advertisement

SpringerLink
Log in

Numerical study and optimization of thermohydraulic characteristics of a graphene–platinum nanofluid in finned annulus using genetic algorithm combined with decision-making technique

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The heat transfer and flow attributes of a cylindrical microchannel heat sink (CMCHS) operated with a hybrid nanofluid containing the graphene nanoplatelets and platinum particles are numerically investigated. The CMCHS is modeled with three different numbers of fins, and the problem is solved for four concentrations and four Reynolds numbers. The effect of these variables on the thermal and frictional parameters—such as the convective heat transfer coefficient, pressure loss, friction coefficient, thermal resistance, and maximum temperature—is evaluated. The heat transfer coefficient increases by raising the Reynolds number, concentration, and fin number. Thereby, with an increase in fin number from 25 to 36 at Reynolds number of 300 and concentration of 0.1%, the convective heat transfer coefficient is enhanced by 134%. The maximum performance evaluation criterion (PEC) is obtained as 1.98 at concentration of 0.1%, Reynolds number of 600, and number of fins of 36. The thermal resistance decreases by increasing each of the parameters of fin number, Reynolds number, and concentration. Based on the obtained data, a predictor model for the output parameters (i.e., heat transfer coefficient, thermal resistance, and pumping power) is derived by a neural network. Then, the optimization is performed using a genetic algorithm combined with decision-making technique considering different designer’s viewpoints to achieve the highest convective heat transfer coefficient and the lowest pumping power and thermal resistance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Abbreviations

A :

Area (m2)

C p :

Specific heat capacity (J/kgK)

d h :

Hydraulic diameter (m)

f :

Friction factor

h :

Convective heat transfer coefficient (W/m2 K)

H :

Height (m)

k :

Thermal conductivity (W/mK)

MRE:

Mean relative error

MSE:

Mean Square Error

N :

Number of fins

Nu :

Nusselt number

P :

Pressure (Pa)

q :

Heat flux (W/m2)

R :

Radius (m)

R Th :

Thermal resistance (K/W)

Re :

Reynolds number

T :

Temperature (K)

v :

Velocity (m/s)

\(\dot{V}\) :

Volumetric flow rate(m3/s)

w :

Width (m)

\(\dot{W}\) :

Pumping power (W)

z :

Axial distance from the inlet (m)

\(\alpha\) :

Relative importance of objective functions

\(\Delta P\) :

Pressure drop (Pa)

\(\theta\) :

Angle (°)

\(\mu\) :

Viscosity (Pa s)

\(\rho\) :

Density (kg/m3)

\(\varphi\) :

Concentration (wt%)

b:

Bulk

c:

Channel

conv:

Convection

f:

Fluid

hnf:

Hybrid nanofluid

in:

Inlet

out:

Outlet

References

  1. Zhao N, Guo L, Qi C, Chen T, Cui X (2019) Experimental study on thermo-hydraulic performance of nanofluids in CPU heat sink with rectangular grooves and cylindrical bugles based on exergy efficiency. Energy Convers Manage 181:235–246

    Google Scholar 

  2. Qi C, Li K, Li C, Shang B, Yan Y (2020) Experimental study on thermal efficiency improvement using nanofluids in heat sink with heated circular cylinder. Int Commun Heat Mass Transfer 114:104589

    Google Scholar 

  3. Tuckerman DB, Pease RFW (1981) High-performance heat sinking for VLSI. IEEE Electron Device Lett 2(5):126–129

    Google Scholar 

  4. Qi C, Hu J, Liu M, Guo L, Rao Z (2017) Experimental study on thermo-hydraulic performances of CPU cooled by nanofluids. Energy Convers Manage 153:557–565

    Google Scholar 

  5. Gunnasegaran P, Mohammed HA, Shuaib NH, Saidur R (2010) The effect of geometrical parameters on heat transfer characteristics of microchannels heat sink with different shapes. Int Commun Heat Mass Transfer 37(8):1078–1086

    Google Scholar 

  6. Wang Z-H, Wang X-D, Yan W-M, Duan Y-Y, Lee D-J, Xu J-L (2011) Multi-parameters optimization for microchannel heat sink using inverse problem method. Int J Heat Mass Transf 54(13–14):2811–2819

    MATH  Google Scholar 

  7. Hasan MI, Tbena HL (2018) Using of phase change materials to enhance the thermal performance of micro channel heat sink. Eng Sci Technol Int J 21(3):517–526

    Google Scholar 

  8. Ganesh N, Dutta P, Ramachandran M et al (2019) Robust metamodels for accurate quantitative estimation of turbulent flow in pipe bends. Eng Comput. https://doi.org/10.1007/s00366-019-00748-7

    Article  Google Scholar 

  9. Dehghan M, Ebrahimi F, Vinyas M (2019) Wave dispersion characteristics of fluid-conveying magneto-electro-elastic nanotubes. Eng Comput. https://doi.org/10.1007/s00366-019-00790-5

    Article  Google Scholar 

  10. Zheng T et al (2019) Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm. Chem Eng J 357:641–654

    Google Scholar 

  11. Azizi Z, Alamdari A, Malayeri MR (2015) Convective heat transfer of Cu–water nanofluid in a cylindrical microchannel heat sink. Energy Convers Manage 101:515–524

    Google Scholar 

  12. Azizi Z, Alamdari A, Malayeri MR (2016) Thermal performance and friction factor of a cylindrical microchannel heat sink cooled by Cu-water nanofluid. Appl Therm Eng 99:970–978

    Google Scholar 

  13. Akbarzadeh P, Fardi A (2018) Natural convection heat transfer in 2D and 3D trapezoidal enclosures filled with nanofluid. J Appl Mech Tech Phys 59(2):292–302

    MathSciNet  Google Scholar 

  14. Qi C, Tang J, Fan F, Yan Y (2020) Effects of magnetic field on thermo-hydraulic behaviors of magnetic nanofluids in CPU cooling system. Appl Therm Eng 179:115717

    Google Scholar 

  15. Ganvir RB, Walke PV, Kriplani VM (2017) Heat transfer characteristics in nanofluid—a review. Renew Sustain Energy Rev 75:451–460

    Google Scholar 

  16. Chein R, Huang G (2005) Analysis of microchannel heat sink performance using nanofluids. Appl Therm Eng 25(17–18):3104–3114

    Google Scholar 

  17. Mohammed H, Gunnasegaran P, Shuaib N (2010) Heat transfer in rectangular microchannels heat sink using nanofluids. Int Commun Heat Mass Transfer 37(10):1496–1503

    Google Scholar 

  18. Ghasemi SE, Ranjbar A, Hosseini M (2017) Numerical study on effect of CuO-water nanofluid on cooling performance of two different cross-sectional heat sinks. Adv Powder Technol 28(6):1495–1504

    Google Scholar 

  19. Hung T-C, Yan W-M, Wang X-D, Chang C-Y (2012) Heat transfer enhancement in microchannel heat sinks using nanofluids. Int J Heat Mass Transf 55(9–10):2559–2570

    Google Scholar 

  20. Safaei MR, Hajizadeh A, Afrand M, Qi C, Yarmand H, Zulkifli NWBM (2019) Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data. Phys A 519:209–216

    Google Scholar 

  21. Sarkar J, Ghosh P, Adil A (2015) A review on hybrid nanofluids: recent research, development and applications. Renew Sustain Energy Rev 43:164–177

    Google Scholar 

  22. Yarmand H et al (2016) Study of synthesis, stability and thermo-physical properties of graphene nanoplatelet/platinum hybrid nanofluid. Int Commun Heat Mass Transfer 77:15–21

    Google Scholar 

  23. Younis A, Elsarrag E, Alhorr Y, Onsa M (2018) The influence of Al2O3-ZnO-H2O nanofluid on the thermodynamic performance of photovoltaic-thermal hybrid solar collector system. Innov Ener Res 7(187):25761463

    Google Scholar 

  24. Bahiraei M, Heshmatian S (2018) Thermal performance and second law characteristics of two new microchannel heat sinks operated with hybrid nanofluid containing graphene–silver nanoparticles. Energy Convers Manage 168:357–370

    Google Scholar 

  25. Bahiraei M, Mazaheri N (2018) Application of a novel hybrid nanofluid containing graphene–platinum nanoparticles in a chaotic twisted geometry for utilization in miniature devices: thermal and energy efficiency considerations. Int J Mech Sci 138:337–349

    Google Scholar 

  26. Ahmadloo E, Azizi S (2016) Prediction of thermal conductivity of various nanofluids using artificial neural network. Int Commun Heat Mass Transfer 74:69–75

    Google Scholar 

  27. Vaferi B, Samimi F, Pakgohar E, Mowla D (2014) Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes. Powder Technol 267:1–10

    Google Scholar 

  28. Safikhani H, Abbassi A, Khalkhali A, Kalteh M (2014) Multi-objective optimization of nanofluid flow in flat tubes using CFD, artificial neural networks and genetic algorithms. Adv Powder Technol 25(5):1608–1617

    Google Scholar 

  29. Bahiraei M, Khosravi R, Heshmatian S (2017) Assessment and optimization of hydrothermal characteristics for a non-Newtonian nanofluid flow within miniaturized concentric-tube heat exchanger considering designer’s viewpoint. Appl Therm Eng 123:266–276

    Google Scholar 

  30. Hajmohammadi M, Alipour P, Parsa H (2018) Microfluidic effects on the heat transfer enhancement and optimal design of microchannels heat sinks. Int J Heat Mass Transf 126:808–815

    Google Scholar 

  31. Morini GL (2005) Viscous heating in liquid flows in micro-channels. Int J Heat Mass Transf 48(17):3637–3647

    MATH  Google Scholar 

  32. Radwan A, Ahmed M, Ookawara S (2016) Performance enhancement of concentrated photovoltaic systems using a microchannel heat sink with nanofluids. Energy Convers Manage 119:289–303

    Google Scholar 

  33. Lin L, Zhao J, Lu G, Wang X-D, Yan W-M (2017) Heat transfer enhancement in microchannel heat sink by wavy channel with changing wavelength/amplitude. Int J Therm Sci 118:423–434

    Google Scholar 

  34. Bayrak E, Olcay AB, Serincan MF (2019) Numerical investigation of the effects of geometric structure of microchannel heat sink on flow characteristics and heat transfer performance. Int J Therm Sci 135:589–600

    Google Scholar 

  35. Chai L, Xia GD, Wang HS (2016) Parametric study on thermal and hydraulic characteristics of laminar flow in microchannel heat sink with fan-shaped ribs on sidewalls–Part 3: performance evaluation. Int J Heat Mass Transf 97:1091–1101

    Google Scholar 

  36. Kays WM (2012) Convective heat and mass transfer. Tata McGraw-Hill Education, New York

    Google Scholar 

  37. Sun Z, Sun L, Yan C, Huang W (2004) Experimental investigation of single-phase flow friction in narrow annuli. Nuclear Power Eng 25(2):123–127

    Google Scholar 

  38. Moosazadeh S, Namazi E, Aghababaei H et al (2019) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Eng Comput 35:579–591

    Google Scholar 

  39. Shukla V, Bandyopadhyay M, Pandya V et al (2020) Artificial neural network based predictive negative hydrogen ion helicon plasma source for fusion grade large sized ion source. Eng Comput. https://doi.org/10.1007/s00366-020-01060-5

    Article  Google Scholar 

  40. Yigit KS, Ertunc HM (2006) Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks. Int Commun Heat Mass Transfer 33(7):898–907

    Google Scholar 

  41. Hemmat Esfe M, Bahiraei M, Mahian O (2018) Experimental study for developing an accurate model to predict viscosity of CuO–ethylene glycol nanofluid using genetic algorithm based neural network. Powder Technol 338:383–390

    Google Scholar 

  42. Kumar DN (2010) Multicriterion analysis in engineering and management. PHI Learning Pvt. Ltd., New Delhi

    Google Scholar 

  43. Singla RK, Das R (2017) Multi-parameter retrieval in a porous fin using binary-coded genetic algorithm. In: Proceedings of sixth international conference on soft computing for problem solving, pp 197–205, Springer, New York

  44. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge

    Google Scholar 

  45. McCall J (2005) Genetic algorithms for modelling and optimisation. J Comput Appl Math 184(1):205–222

    MathSciNet  MATH  Google Scholar 

  46. Khosravanian R, Mansouri V, Wood DA, Alipour MR (2018) A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. J Pet Explor Prod Technol 8(4):1487–1503

    Google Scholar 

Download references

Acknowledgements

The authors sincerely thank Elmira Bahrami Majd (Department of English, Simon Fraser University, Burnaby, Canada) for her efficient and excellent language editing on this paper.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Raouf Khosravi, A. R. Teymourtash, Mohammad Passandideh Fard & Saeed Rabiei

  2. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mehdi Bahiraei

Authors
  1. Raouf Khosravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. R. Teymourtash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Passandideh Fard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saeed Rabiei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mehdi Bahiraei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to A. R. Teymourtash or Mehdi Bahiraei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khosravi, R., Teymourtash, A.R., Passandideh Fard, M. et al. Numerical study and optimization of thermohydraulic characteristics of a graphene–platinum nanofluid in finned annulus using genetic algorithm combined with decision-making technique. Engineering with Computers 37, 2473–2491 (2021). https://doi.org/10.1007/s00366-020-01178-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01178-6

Keywords

Search

Navigation