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Response surface methodology-based multi-objective grey relation optimization for impinging jet cooling with Al2O3/water nanofluid on a curved surface

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Abstract

In this study, a modelling procedure is followed to simultaneously optimize the heat transfer and entropy generation performance in the impinging jet flow on a convex surface. For this optimization study, numerical results of laminar nanofluid flow (Al2O3/water) having two different particle shape (blade and cylindrical) were used as inputs and optimum Nusselt number and total entropy generation parameters were obtained by using RSM-based multi-objective grey relation analysis. Different nanofluid volume fractions, target distance/nozzle diameter ratio (H/B) and Reynolds numbers were evaluated for the analysis. Each control variable has three levels except for the particle shape (blade and cylindrical). In total, forty CFD analyses have been performed based on these variables and the findings obtained by the RSM-based multi-objective grey relation analysis reveal that geometric differences and nanofluid properties have a considerable impact on the performance of jet impingement cooling. The results show that the H/B ratio has the greatest impact on heat transfer enhancement and entropy generation improvement on convex surface at laminar flow conditions. As a result of the optimization, the highest Nu number and the lowest entropy generation obtained by grey relation analysis were found to be 4.383 and 9.06 × 10–4 kj/kg K, respectively, for blade-shaped alumina nanofluid at H/B = 2.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Be :

Bejan number [-]

C P :

Specific heat [J/kgK]

D h :

Hydraulic diameter [m]

f :

Darcy friction factor [-]

h :

Convective heat transfer coefficient [W/m2K]

k :

Conductive heat transfer coefficient [W/mK]

\(\dot{m}\) :

Mass flow rate [kg/s]

Nu :

Nusselt number

P :

Pressure [Pa]

Pr :

Prandtl number [Pa]

\(q^{\prime \prime}\) :

Heat flux [W/m2]

Re :

Reynolds number

\(\dot{S}^{\prime}_{gen}\) :

Entropy generation rate per unit length [W/mK]

T :

Temperature [K]

V :

Velocity vector

\(\mu\) :

Dynamic viscosity [Pas]

\(\rho\) :

Density [kg/m3]

\(\varphi\) :

Nanoparticle volume fraction [-]

b:

Bulk

bf:

Base fluid

gen:

Generation

hnf:

Hybrid nanofluid

w:

Wall

References

  1. Kursuncu B, Biyik YE (2021) Optimization of cutting parameters with Taguchi and grey relational analysis methods in MQL-assisted face milling of AISI O2 steel. J Cent South Univ. https://doi.org/10.1007/s11771-021-4590-4

    Article  Google Scholar 

  2. Karimifard S, Alavi Moghaddam MR (2018) Application of response surface methodology in physicochemical removal of dyes from wastewater: a critical review. Sci Total Environ 640–641:772–797. https://doi.org/10.1016/J.SCITOTENV.2018.05.355

    Article  Google Scholar 

  3. Ferreira SLC, Bruns RE, Ferreira HS et al (2007) Box-Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta 597:179–186

    Article  Google Scholar 

  4. Behbahani M, Moghaddam MRA, Arami M (2011) Techno-economical evaluation of fluoride removal by electrocoagulation process: optimization through response surface methodology. Desalination 271:209–218

    Article  Google Scholar 

  5. Manca O, Ricci D, Nardini S, Di Lorenzo G (2016) Thermal and fluid dynamic behaviors of confined laminar impinging slot jets with nanofluids. Int Commun Heat Mass Transfer 70:15–26. https://doi.org/10.1016/j.icheatmasstransfer.2015.11.010

    Article  Google Scholar 

  6. Kim YH, Lee DH, Han SH (2017) Investigation of impingement surface geometry effects on heat transfer in a laminar confined impinging slot jet. Int J Heat Mass Transf 115:347–353. https://doi.org/10.1016/j.ijheatmasstransfer.2017.08.070

    Article  Google Scholar 

  7. Rajabi Zargarabadi M, Rezaei E, Yousefi-Lafouraki B (2018) Numerical analysis of turbulent flow and heat transfer of sinusoidal pulsed jet impinging on an asymmetrical concave surface. Appl Therm Eng 128:578–585. https://doi.org/10.1016/j.applthermaleng.2017.09.059

    Article  Google Scholar 

  8. Li Y, Li B, Qi F, Cheung SCP (2018) Flow and heat transfer of parallel multiple jets obliquely impinging on a flat surface. Appl Therm Eng 133:588–603. https://doi.org/10.1016/j.applthermaleng.2018.01.064

    Article  Google Scholar 

  9. Wienand J, Riedelsheimer A, Weigand B (2017) Numerical study of a turbulent impinging jet for different jet-to-plate distances using two-equation turbulence models. Eur J Mech B/Fluids 61:210–217. https://doi.org/10.1016/j.euromechflu.2016.09.008

    Article  MathSciNet  MATH  Google Scholar 

  10. Bentarzi F, Mataoui A, Rebay M (2019) Effect of inclination of twin impinging turbulent jets on flow and heat transfer characteristics. Int J Therm Sci 137:490–499. https://doi.org/10.1016/j.ijthermalsci.2018.12.021

    Article  Google Scholar 

  11. Zunaid M, Cho HM, Husain A et al (2018) Computational analysis of liquid jet impingement microchannel cooling. Mater Today Proc 5:27877–27883. https://doi.org/10.1016/j.matpr.2018.10.026

    Article  Google Scholar 

  12. Cademartori S, Cravero C, Marini M, Marsano D (2021) CFD simulation of the slot jet impingement heat transfer process and application to a temperature control system for galvanizing line of metal band. Appl Sci 11:1–23. https://doi.org/10.3390/app11031149

    Article  Google Scholar 

  13. Issac J, Singh D, Kango S (2020) Experimental and numerical investigation of heat transfer characteristics of jet impingement on a flat plate. Heat and Mass Transfer 56:531–546. https://doi.org/10.1007/s00231-019-02724-9

    Article  Google Scholar 

  14. Huang H, Sun T, Zhang G et al (2018) Modeling and computation of turbulent slot jet impingement heat transfer using RANS method with special emphasis on the developed SST turbulence model. Int J Heat Mass Transf 126:589–602. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.121

    Article  Google Scholar 

  15. Yang B, Chang S, Wu H et al (2017) Experimental and numerical investigation of heat transfer in an array of impingement jets on a concave surface. Appl Therm Eng 127:473–483. https://doi.org/10.1016/j.applthermaleng.2017.07.190

    Article  Google Scholar 

  16. Celik N, Pusat G, Turgut E (2018) Application of Taguchi method and grey relational analysis on a turbulated heat exchanger. Int J Therm Sci 124:85–97. https://doi.org/10.1016/j.ijthermalsci.2017.10.007

    Article  Google Scholar 

  17. Fu J, Cai J (2020) Study of heat transfer and the hydrodynamic performance of gas-solid heat transfer in a vertical sinter cooling bed using the CFD-taguchi-grey relational analysis method. Energies (Basel). https://doi.org/10.3390/en13092225

    Article  Google Scholar 

  18. Sridharan M (2022) Performance optimization of counter flow double pipe heat exchanger using grey relational analysis. Int J Ambient Energ 43:5318–5326. https://doi.org/10.1080/01430750.2021.1946148

    Article  Google Scholar 

  19. Garud KS, Lee MY (2023) Grey relational based Taguchi analysis on heat transfer performances of direct oil spray cooling system for electric vehicle driving motor. Int J Heat Mass Transf. https://doi.org/10.1016/j.ijheatmasstransfer.2022.123596

    Article  Google Scholar 

  20. Chamoli S, Yu P, Kumar A (2016) Multi-response optimization of geometric and flow parameters in a heat exchanger tube with perforated disk inserts by Taguchi grey relational analysis. Appl Therm Eng 103:1339–1350. https://doi.org/10.1016/j.applthermaleng.2016.04.166

    Article  Google Scholar 

  21. Çinici OK, Canlı ME, Çakıroğlu R, Acır A (2022) Optimization of melting time of solar thermal energy storage unit containing spring type heat transfer enhancer by Taguchi based grey relational analysis. J Energ Storage. https://doi.org/10.1016/j.est.2021.103671

    Article  Google Scholar 

  22. Razak Kaladgi A, Afzal A, Manokar AM et al (2021) Integrated Taguchi-GRA-RSM optimization and ANN modelling of thermal performance of zinc oxide nanofluids in an automobile radiator. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2021.101068

    Article  Google Scholar 

  23. Dagdevir T (2022) Multi-objective optimization of geometrical parameters of dimples on a dimpled heat exchanger tube by Taguchi based Grey relation analysis and response surface method. Int J Therm Sci. https://doi.org/10.1016/j.ijthermalsci.2021.107365

    Article  Google Scholar 

  24. Barroso-Maldonado JM, Belman-Flores JM, Ledesma S, Aceves SM (2018) Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks. Cryogenics (Guildf) 92:60–70. https://doi.org/10.1016/j.cryogenics.2018.04.005

    Article  Google Scholar 

  25. Mahanthesh B, Shehzad SA, Mackolil J, Shashikumar NS (2021) Heat transfer optimization of hybrid nanomaterial using modified Buongiorno model: a sensitivity analysis. Int J Heat Mass Transf. https://doi.org/10.1016/j.ijheatmasstransfer.2021.121081

    Article  Google Scholar 

  26. Kareem ZS, Balla HH, Abdulwahid AF (2019) Heat transfer enhancement in single circular impingement jet by CuO-water nanofluid. Case Stud Therm Eng 15:100508. https://doi.org/10.1016/j.csite.2019.100508

    Article  Google Scholar 

  27. Sun B, Zhang Y, Yang D, Li H (2019) Experimental study on heat transfer characteristics of hybrid nanofluid impinging jets. Appl Therm Eng 151:556–566. https://doi.org/10.1016/j.applthermaleng.2019.01.111

    Article  Google Scholar 

  28. Vanaki SM, Mohammed HA, Abdollahi A, Wahid MA (2014) Effect of nanoparticle shapes on the heat transfer enhancement in a wavy channel with different phase shifts. J Mol Liq 196:32–42. https://doi.org/10.1016/j.molliq.2014.03.001

    Article  Google Scholar 

  29. Timofeeva EV, Routbort JL, Singh D (2009) Particle shape effects on thermophysical properties of alumina nanofluids. J Appl Phys. https://doi.org/10.1063/1.3155999

    Article  Google Scholar 

  30. Es HA, Hamzacebi C, Firat SUO (2018) GRA-TRI: a multicriteria decision aid classification method based on grey relational analysis. J Grey Syst 30:1–12

    Google Scholar 

  31. Mia M, Dhar NR (2016) Prediction of surface roughness in hard turning under high pressure coolant using artificial neural network. Measurement 92:464–474. https://doi.org/10.1016/j.measurement.2016.06.048

    Article  Google Scholar 

  32. Mia M, Razi MH, Ahmad I et al (2017) Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network. Int J Adv Manuf Technol 91:3211–3223. https://doi.org/10.1007/s00170-016-9978-1

    Article  Google Scholar 

  33. Ustaoglu A, Kursuncu B, Alptekin M, Gok MS (2020) Performance optimization and parametric evaluation of the cascade vapor compression refrigeration cycle using Taguchi and ANOVA methods. Appl Therm Eng 180:115816. https://doi.org/10.1016/j.applthermaleng.2020.115816

    Article  Google Scholar 

  34. Maruda RW, Krolczyk GM, Nieslony P et al (2016) The influence of the cooling conditions on the cutting tool wear and the chip formation mechanism. J Manuf Process 24:107–115. https://doi.org/10.1016/j.jmapro.2016.08.006

    Article  Google Scholar 

  35. Varghese V, Annamalai K, Kumar KS (2014) Optimisation of machining parameters for high feed end milling on inconel 718 super alloy. Appl Mech Mater 592–594:584–590. https://doi.org/10.4028/www.scientific.net/AMM.592-594.584

    Article  Google Scholar 

  36. Greco CS, Paolillo G, Ianiro A et al (2018) Effects of the stroke length and nozzle-to-plate distance on synthetic jet impingement heat transfer. Int J Heat Mass Transf 117:1019–1031. https://doi.org/10.1016/j.ijheatmasstransfer.2017.09.118

    Article  Google Scholar 

  37. Naphon P, Nakharintr L, Wiriyasart S (2018) Continuous nanofluids jet impingement heat transfer and flow in a micro-channel heat sink. Int J Heat Mass Transf 126:924–932. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.101

    Article  Google Scholar 

  38. Kaya H, Arslan K, Eltugral N (2018) Experimental investigation of thermal performance of an evacuated U-tube solar collector with ZnO/Etylene glycol-pure water nanofluids. Renew Energy 122:329–338. https://doi.org/10.1016/j.renene.2018.01.115

    Article  Google Scholar 

  39. Kaya H, Arslan K (2019) Numerical investigation of efficiency and economic analysis of an evacuated U-tube solar collector with different nanofluids. Heat and Mass Transfer 55:581–593. https://doi.org/10.1007/s00231-018-2442-z

    Article  Google Scholar 

  40. Moosavi M, Goharshadi EK, Youssefi A (2010) Fabrication, characterization, and measurement of some physicochemical properties of ZnO nanofluids. Int J Heat Fluid Flow 31:599–605. https://doi.org/10.1016/j.ijheatfluidflow.2010.01.011

    Article  Google Scholar 

  41. Li Y, Fernández-Seara J, Du K et al (2016) Experimental investigation on heat transfer and pressure drop of ZnO/ethylene glycol-water nanofluids in transition flow. Appl Therm Eng 93:537–548. https://doi.org/10.1016/j.applthermaleng.2015.09.020

    Article  Google Scholar 

  42. Vajjha RS, Das DK (2009) Experimental determination of thermal conductivity of three nanofluids and development of new correlations. Int J Heat Mass Transf 52:4675–4682. https://doi.org/10.1016/j.ijheatmasstransfer.2009.06.027

    Article  MATH  Google Scholar 

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  1. Faculty of Engineering, Architecture and Design, Mechanical Engineering, Bartin University, 74100, Bartin, Turkey

    Volkan Akgül, Bilal Kurşuncu & Hüseyin Kaya

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Akgül, V., Kurşuncu, B. & Kaya, H. Response surface methodology-based multi-objective grey relation optimization for impinging jet cooling with Al2O3/water nanofluid on a curved surface. Neural Comput & Applic 35, 13999–14012 (2023). https://doi.org/10.1007/s00521-023-08357-8

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