Skip to main content
SpringerLink
Log in

Dynamic viscosity analysis of hybrid nanofluid MWCNT- Al2O3/engine oil using statistical models with evaluating its performance in a double tube heat exchanger

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

Hybrid nanofluids are a new generation for improved and more controlled heat transfer. In this research, firstly, the effects of volume concentration in the range of 0.13–1%, temperature between 5 °C and 55 \(^\circ C\), and spindle rotation speed in the range of 200–600 rpm are first experimentally assessed on the dynamic viscosity of a hybrid nanofluid composed of multi-walled carbon nanotube (15%) and aluminum oxide (85%) nanoparticles suspended in engine oil. Then, a relationship is obtained for dynamic viscosity related to volume fraction, temperature, and spindle rotation speed. The general linear model (GLM) powered by analysis of variance (ANOVA) and response surface methodology (RSM) statistical model is also employed to identify the essential factors of the experiment and define correlations to predict the results, respectively. The statistical regression achieves a correlation of 80% by a linear model, 97% by a quadratic model, and 98% by a nonlinear model. Finally, as the novelty of the work, the nanofluid is implemented experimentally in a double tube heat exchanger with a spiral baffle. This part of the research evaluates the effects of parameters such as hot and cold working fluids' flow rates and intake temperatures. The results indicate that temperature and mass fraction impact viscosity more than shear rate. The non-Newtonian behavior of the studied hybrid nanofluid is observed for temperatures of 5 °C and 15 °C. The predictions are reasonably accurate. The study demonstrated that the best heat exchanger effectiveness could be achieved when all nanofluid flow and moderate to high water flow rates are considered hot and cold working fluids, respectively.

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

Similar content being viewed by others

Abbreviations

a :

Accuracy of the measurement equipment

A :

Total heat transfer area

Adj MS:

Adjusted mean of squares

Adj SS:

Adjusted sum of squares

C p :

Specific heat (kJ kg−1 K−1)

\(f\) :

Predicted response

\(\Delta t_{{\text{m}}}\) :

Mean logarithmic temperature difference

DF:

Degree of freedom

F :

F-Statistic

\(\dot{m}\) :

Mass flow rate (kg s−1)

P :

P-value

\(\dot{Q}\) :

Heat transfer rate (W)

Seq SS:

Sequential sum of squares

SS:

Sum of Squares

T :

Temperature (°C)

U :

Heat transfer coefficient of heat exchanger (W m−2 K−1)

u :

Uncertainty

x :

Independent variable for uncertainty analysis

\(X_{\rm i}\) :

Uncoded values of the process variables

y :

Dependent variable for uncertainty analysis

\(y_{\rm i}\) :

ith Observed data

\(\mu_{\rm nf}\) :

Dynamic viscosity of nanofluid (cP)

\(\gamma\) :

Spindle rotation speed (rpm)

\(\varphi\) :

Volume fraction

\(\varepsilon_{\rm th}\) :

Thermal effectiveness of heat exchanger

c :

Cold flow

h :

Hot flow

in :

Inlet

out :

Outlet

res :

Residual sum of squares

References

  1. Chen H, Ding Y, Lapkin A. Rheological behaviour of nanofluids containing tube/rod-like nanoparticles. Powder Technol. 2009;194(1–2):132–41.

    Article  CAS  Google Scholar 

  2. Alizadeh R, Karimi N, Arjmandzadeh R, Mehdizadeh A. Mixed convection and thermodynamic irreversibilities in MHD nanofluid stagnation-point flows over a cylinder embedded in porous media. J Thermal Anal Calorimet. 2019;135(1):489–506.

    Article  CAS  Google Scholar 

  3. Alizadeh R, Dorfaki V, Ameri A, Valizadeh Ardalan M, Javidi Sarafan M. Heat transfer and pressure drop in a sinus blowing of copper oxide-water non-Newtonian nanofluid in a sudden expansion process in the presence of variable magnetic field: a numerical solution. Energy Sources, Part A: Recovery, Utilization, and Environ Effects. 2020 pp. 1-24

  4. Reich S, Thomsen C, Maultzsch J, Carbon nanotubes: basic concepts and physical properties. John Wiley & Sons, 2008.

  5. Phanindra Y, Kumar S, Pugazhendhi S. Experimental investigation on Al2O3 & Cu/Oil hybrid nano fluid using concentric tube heat exchanger. Mater Today: Proceed. 2018;5(5):12142–50.

    Article  CAS  Google Scholar 

  6. Mechiri SK, Vasu V, Gopal AV. Rheological study of Cu-Zn Hybrid newtonian nano-fluids: experimental data and modelling using neural network. Mater Today: Proceed. 2017;4(2):1957–63.

    Article  Google Scholar 

  7. Plant RD, Hodgson GK, Impellizzeri S, Saghir MZ. Experimental and numerical investigation of heat enhancement using a hybrid nanofluid of copper oxide/alumina nanoparticles in water. J Therm Anal Calorimet. 2020;141:1951–68.

    Article  CAS  Google Scholar 

  8. Ekiciler R, Arslan K, Turgut O, Kurşun B. Effect of hybrid nanofluid on heat transfer performance of parabolic trough solar collector receiver. J Therm Anal Calorimet. 2021;143:1637–54.

    Article  CAS  Google Scholar 

  9. Ekiciler R. Effects of novel hybrid nanofluid (TiO2–Cu/EG) and geometrical parameters of triangular rib mounted in a duct on heat transfer and flow characteristics,". J Therm Anal Calorimet. 2021;143:1371–87.

    Article  CAS  Google Scholar 

  10. Ghadikolaei S, Gholinia M, Hoseini M, Ganji D. Natural convection MHD flow due to MoS2–Ag nanoparticles suspended in C2H6O2H2O hybrid base fluid with thermal radiation. J Taiwan Inst Chem Eng. 2019;97:12–23.

    Article  CAS  Google Scholar 

  11. Dalkılıç AS, et al. Experimental investigation on the viscosity characteristics of water based SiO2-graphite hybrid nanofluids. Int Commun Heat Mass Trans. 2018;97:30–8.

    Article  Google Scholar 

  12. Maraj E, Iqbal Z, Azhar E, Mehmood Z. A comprehensive shape factor analysis using transportation of MoS2-SiO2/H2O inside an isothermal semi vertical inverted cone with porous boundary. Results in physics. 2018;8:633–41.

    Article  Google Scholar 

  13. Hayat T, Nadeem S. Heat transfer enhancement with Ag–CuO/water hybrid nanofluid. Results in Phys. 2017;7:2317–24.

    Article  Google Scholar 

  14. Iqbal Z, Akbar N, Azhar E, Maraj E. Performance of hybrid nanofluid (Cu-CuO/water) on MHD rotating transport in oscillating vertical channel inspired by Hall current and thermal radiation. Alex Eng J. 2018;57(3):1943–54.

    Article  Google Scholar 

  15. Moldoveanu GM, Minea AA, Iacob M, Ibanescu C, Danu M. Experimental study on viscosity of stabilized Al2O3, TiO2 nanofluids and their hybrid. Thermochim Acta. 2018;659:203–12.

    Article  CAS  Google Scholar 

  16. Sharma S, Tiwari A, Tiwari S, Prakash R. Viscosity of hybrid nanofluids: measurement and comparison. J Mech Eng Sci. 2018;12(2):3614–23.

    Article  CAS  Google Scholar 

  17. Sahid N, Rahman M, Kadirgama K, Maleque M. Experimental investigation on properties of hybrid nanofluids (TiO2 and ZnO) in water–ethylene glycol mixture. J Mech Eng Sci. 2017;11(4):3087–94.

    Article  CAS  Google Scholar 

  18. Çiftçi E. Distilled Water-Based AlN + ZnO Binary Hybrid Nanofluid Utilization in a Heat Pipe and Investigation of Its Effects on Performance. Int J Thermophys. 2021;42:38.

    Article  Google Scholar 

  19. Martin K, Sözen A, Çiftçi E, Ali HM. An Experimental Investigation on Aqueous Fe–CuO Hybrid Nanofluid Usage in a Plain Heat Pipe. Int J Thermophys. 2020;41:135.

    Article  CAS  Google Scholar 

  20. Nasirzadehroshenin F, Maddah H, Sakhaeinia H, Pourmozafari A. Investigation of exergy of double-pipe heat exchanger using synthesized hybrid nanofluid developed by modeling,". Int J Thermophys. 2019;40(9):87.

    Article  Google Scholar 

  21. Javadpour SM, Abbasi Jannat AbadiAkbariGoharimanesh EOAM. Optimization of geometry and nano-fluid properties on microchannel performance using Taguchi method and genetic algorithm,". Int Commun Heat and Mass Trans. 2020;119:104952.

    Article  CAS  Google Scholar 

  22. Goharimanesh M, Abbasi Jannatabadi E, Dehghani M, Javadpour SM. Geometric and thermo hydrodynamic investigation of a 3D converging-diverging channel by Taguchi and ANFIS methods,". Int Commun Heat and Mass Trans. 2022;138:106285.

    Article  CAS  Google Scholar 

  23. Esfahani JA, et al. Comparison of experimental data, modelling and non-linear regression on transport properties of mineral oil based nanofluids,". Powder Technol. 2017;317:458–70.

    Article  CAS  Google Scholar 

  24. Liu W, Al-Rashed AA, Alsagri AS, Mahmoudi B, Shahsavar A, Afrand M. Laminar forced convection performance of non-Newtonian water-CNT/Fe3O4 nano-fluid inside a minichannel hairpin heat exchanger: effect of inlet temperature. Powder Technol. 2019;354:247–58.

    Article  CAS  Google Scholar 

  25. Hemmat Esfe M, Rahimi Raki H, Sarmasti Emami MR, Afrand M. Viscosity and rheological properties of antifreeze based nanofluid containing hybrid nano-powders of MWCNTs and TiO2 under different temperature conditions,". Powder Technol. 2019;342:808–16.

    Article  CAS  Google Scholar 

  26. Asadi M, Asadi A, Aberoumand S. An experimental and theoretical investigation on the effects of adding hybrid nanoparticles on heat transfer efficiency and pumping power of an oil-based nanofluid as a coolant fluid,". Int J Refrigerat. 2018;89:83–92.

    Article  CAS  Google Scholar 

  27. Pourrajab R, Noghrehabadi A, Behbahani M, Hajidavalloo E. An efficient enhancement in thermal conductivity of water-based hybrid nanofluid containing MWCNTs-COOH and Ag nanoparticles: experimental study,". J Therm Anal Calorimet. 2021;143(5):3331–43.

    Article  CAS  Google Scholar 

  28. Wu H, Al-Rashed AAAA, Barzinjy AA, Shahsavar A, Karimi A, Talebizadehsardari P. Curve-fitting on experimental thermal conductivity of motor oil under influence of hybrid nano additives containing multi-walled carbon nanotubes and zinc oxide,". Physica A: Statistical Mech Appl. 2019;535:122128.

    Article  CAS  Google Scholar 

  29. Bagherzadeh SA, D’Orazio A, Karimipour A, Goodarzi M, Bach Q-V. A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs,". Physica A: Statistical Mech Appl. 2019;521:406–15.

    Article  CAS  Google Scholar 

  30. Jana S, Salehi-Khojin A, Zhong W-H. Enhancement of fluid thermal conductivity by the addition of single and hybrid nano-additives,". Thermochimica Acta. 2007;462(1):45–55.

    Article  CAS  Google Scholar 

  31. Esfe MH, Rejvani M, Karimpour R, Abbasian Arani AA. Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data,". J Therm Anal Calorimet. 2017;128(3):1359–71.

    Article  CAS  Google Scholar 

  32. Mohebbi Najm AbadAlizadehFattahiDoranehgardAlhajriKarimi JRAMHEN. Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization,". J Molecular Liquids. 2020;313:113492.

    Article  Google Scholar 

  33. Ruhani B, Toghraie D, Hekmatifar M, Hadian M. Statistical investigation for developing a new model for rheological behavior of ZnO–Ag (50%–50%)/Water hybrid Newtonian nanofluid using experimental data,". Physica A: Statistical Mech Appl. 2019;525:741–51.

    Article  CAS  Google Scholar 

  34. Ruhani B, Barnoon P, Toghraie D. Statistical investigation for developing a new model for rheological behavior of Silica–ethylene glycol/Water hybrid Newtonian nanofluid using experimental data,". Physica A: Statistical Mech Appl. 2019;525:616–27.

    Article  CAS  Google Scholar 

  35. Jamei M, Olumegbon IA, Karbasi M, Ahmadianfar I, Asadi A, Mosharaf-Dehkordi M. On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network,". Int J Heat and Mass Trans. 2021;172:121159.

    Article  CAS  Google Scholar 

  36. Valizadeh Ardalan M, Alizadeh R, Ameri A, Alizadeh A, Mohammad Jafari F (2020) Influence of grooves geometric parameters on the nanofluid flow and thermal efficiency of Chevron plate heat exchangers. Energy Sources, Part A: Recovery, Utilization, and Environ Effects, pp. 1–22

  37. Kharissova OV, Kharisov BI, Valdés JJR, Méndez UO. Ultrasound in nanochemistry: recent advances,". Synthesis and React Inorganic, Metal-Organic, and Nano-Metal Chem. 2011;41:429–48.

    Article  CAS  Google Scholar 

  38. Peer Schmidt MB, Glaum R, Schmidt M (2013) Chemical Vapor Transport Reactions–Methods, Materials, Modeling. Croatia Rijeka : InTech,

  39. Farajimotlagh M, Poursalehi R, Aliofkhazraei M. Synthesis mechanisms, optical and structural properties of η-Al2O3 based nanoparticles prepared by DC arc discharge in environmentally friendly liquids,". Ceramics Int. 2017;43(10):7717–23.

    Article  CAS  Google Scholar 

  40. Heydari A, Shateri M, Sanjari S. Numerical analysis of a small size baffled shell-and-tube heat exchanger using different nano-fluids,". Heat Trans Eng. 2018;39(2):141–53.

    Article  CAS  Google Scholar 

  41. Kirkup L, Frenkel RB. An introduction to uncertainty in measurement: using the GUM (Guide to the expression of uncertainty in measurement). Cambridge: Cambridge University Press; 2006.

    Book  Google Scholar 

  42. Lira I (2003) Evaluating the measurement uncertainty: fundamentals and practical guidance," ed: American Association of Physics Teachers.

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Khorasan Razavi, Iran

    Ali Heydari, Masoud Goharimanesh & Mohammad Reza Gharib

Authors
  1. Ali Heydari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masoud Goharimanesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Reza Gharib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Heydari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heydari, A., Goharimanesh, M. & Gharib, M.R. Dynamic viscosity analysis of hybrid nanofluid MWCNT- Al2O3/engine oil using statistical models with evaluating its performance in a double tube heat exchanger. J Therm Anal Calorim 148, 8025–8039 (2023). https://doi.org/10.1007/s10973-022-11608-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-022-11608-w

Keywords

Search

Navigation