Skip to main content
SpringerLink
Log in

Improving the thermal conductivity of paraffin by incorporating MWCNTs nanoparticles

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

Abstract

In this study, the efficacy of adding nanoparticles of MWCNTs to the base fluid of paraffin on the thermal conductivity has been investigated. MWCNTs-paraffin samples were prepared at different mass fractions using two-step method. Due to the dependency of thermal conductivity on temperature, experiments were performed at temperatures of 25, 40, 55 and 70 °C. It was found that at any temperature and nanotubes concentration, the incorporation of nanotubes into the base fluid improves the thermal conductivity. Based on the results, at the temperature of 70 °C, the incorporation of nanotubes with a mass fraction of 5% causes up to 40.6% improvement in thermal conductivity. It can be concluded that at higher temperatures, the addition of nanotubes has more positive effects on the thermal conductivity. Also, the higher the mass fraction of nanotubes, the greater the sensitivity of thermal conductivity to temperature.

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

Similar content being viewed by others

References

  1. Nguyen Q, Naghieh A, Kalbasi R, Akbari M, Karimipour A, Tlili I. Efficacy of incorporating PCMs into the commercial wall on the energy-saving annual thermal analysis. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09713-9.

    Article  Google Scholar 

  2. Kalbasi R, Afrand M, Alsarraf J, Tran M-D. Studies on optimum fins number in PCM-based heat sinks. Energy. 2019;171:1088–99.

    Article  Google Scholar 

  3. Cengel Y. Heat and mass transfer: fundamentals and applications. New York: McGraw-Hill Higher Education; 2014.

    Google Scholar 

  4. Ahmadi Nadooshan A, Kalbasi R, Afrand M. Perforated fins effect on the heat transfer rate from a circular tube by using wind tunnel: an experimental view. Heat Mass Transf. 2018;54:3047–57.

    Article  CAS  Google Scholar 

  5. Li D, Wu Y, Wang B, Liu C, Arıcı M. Optical and thermal performance of glazing units containing PCM in buildings: a review. Constr Build Mater. 2020;233:117327.

    Article  CAS  Google Scholar 

  6. Etedali S, Afrand M, Abdollahi A. Effect of different surfactants on the pool boiling heat transfer of SiO2/deionized water nanofluid on a copper surface. Int J Therm Sci. 2019;145:105977.

    Article  CAS  Google Scholar 

  7. Salimpour MR, Kalbasi R, Lorenzini G. Constructal multi-scale structure of PCM-based heat sinks. Continuum Mech Thermodyn. 2017;29:477–91.

    Article  CAS  Google Scholar 

  8. Eanest Jebasingh B, Valan Arasu A. A comprehensive review on latent heat and thermal conductivity of nanoparticle dispersed phase change material for low-temperature applications. Energy Storage Mater. 2020;24:52–74.

    Article  Google Scholar 

  9. Singh Rathore PK, Shukla SK, Gupta NK. Potential of microencapsulated PCM for energy savings in buildings: a critical review. Sustain Cities Soc. 2020;53:101884.

    Article  Google Scholar 

  10. Toghraie D, Sina N, Jolfaei NA, Hajian M, Afrand M. Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles. Physica A. 2019;534:122142.

    Article  CAS  Google Scholar 

  11. Yan S-R, Fazilati MA, Samani N, Ghasemi HR, Toghraie D, Nguyen Q, Karimipour A. Energy efficiency optimization of the waste heat recovery system with embedded phase change materials in greenhouses: a thermo-economic-environmental study. J Energy Storage. 2020;30:101445.

    Article  Google Scholar 

  12. Yan S-R, Gholami T, Amiri O, Salavati-Niasari M, Seifi S, Amiri M, Sabet M, Foong LK. Effect of adding TiO2, SiO2 and graphene on of electrochemical hydrogen storage performance and coulombic efficiency of CoAl2O4 spinel. J Alloys Compd. 2020;828:154353.

    Article  CAS  Google Scholar 

  13. Yan S-R, Kalbasi R, Nguyen Q, Karimipour A. Sensitivity of adhesive and cohesive intermolecular forces to the incorporation of MWCNTs into liquid paraffin: experimental study and modeling of surface tension. J Mol Liq. 2020. https://doi.org/10.1016/j.molliq.2020.113235.

    Article  Google Scholar 

  14. Yan S-R, Kalbasi R, Nguyen Q, Karimipour A. Rheological behavior of hybrid MWCNTs-TiO2/EG nanofluid: a comprehensive modeling and experimental study. J Mol Liq. 2020;308:113058.

    Article  CAS  Google Scholar 

  15. Yan S-R, Shirani N, Zarringhalam M, Toghraie D, Nguyen Q, Karimipour A. Prediction of boiling flow characteristics in rough and smooth microchannels using molecular dynamics simulation: investigation the effects of boundary wall temperatures. J Mol Liq. 2020;306:112937.

    Article  CAS  Google Scholar 

  16. Yan S-R, Toghraie D, Hekmatifar M, Miansari M, Rostami S. Molecular dynamics simulation of Water-Copper nanofluid flow in a three-dimensional nanochannel with different types of surface roughness geometry. J Mol Liq. 2020. https://doi.org/10.1016/j.molliq.2020.113222.

    Article  Google Scholar 

  17. Yan S-R, Zarringhalam M, Toghraie D, Foong LK, Talebizadehsardari P. Numerical investigation of non-Newtonian blood flow within an artery with cone shape of stenosis in various stenosis angles. Comput Methods Programs Biomed. 2020;192:105434.

    Article  PubMed  Google Scholar 

  18. Tian Z, Rostami S, Taherialekouhi R, Karimipour A, Moradikazerouni A, Yarmand H, et al. Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experimental data. Phys Stat Mech Appl. 2020;549:124101.

    Article  CAS  Google Scholar 

  19. Liu X, Mohammed HI, Ashkezari AZ, Shahsavar A, Hussein AK, Rostami S. An experimental investigation on the rheological behavior of nanofluids made by suspending multi-walled carbon nanotubes in liquid paraffin. J Mol Liq. 2020;300:112269.

    Article  CAS  Google Scholar 

  20. Tian X-X, Kalbasi R, Jahanshahi R, Qi C, Huang H-L, Rostami S. Competition between intermolecular forces of adhesion and cohesion in the presence of graphene nanoparticles: Investigation of graphene nanosheets/ethylene glycol surface tension. J Mol Liq. 2020. https://doi.org/10.1016/j.molliq.2020.113329.

    Article  Google Scholar 

  21. Ma J, Shahsavar A, Al-Rashed AAAA, Karimipour A, Yarmand H, Rostami S. Viscosity, cloud point, freezing point and flash point of zinc oxide/SAE50 nanolubricant. J Mol Liq. 2020;298:112045.

    Article  CAS  Google Scholar 

  22. Rostami S, Toghraie D, Shabani B, et al. Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs). J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09458-5.

    Article  Google Scholar 

  23. Zheng Y, Zhang X, Shahsavar A, Nguyen Q, Rostami S. Experimental evaluating the rheological behavior of ethylene glycol under graphene nanosheets loading. Powder Technol. 2020;367:788–95.

    Article  CAS  Google Scholar 

  24. Rostami S, Toghraie D, Esfahani MA. et al. Predict the thermal conductivity of SiO2/water–ethylene glycol (50:50) hybrid nanofluid using artificial neural network. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09426-z.

    Article  Google Scholar 

  25. Li Y, Kalbasi R, Nguyen Q, Afrand M. Effects of sonication duration and nanoparticles concentration on thermal conductivity of silica-ethylene glycol nanofluid under different temperatures: an experimental study. Powder Technol. 2020;367:464–73.

    Article  CAS  Google Scholar 

  26. Li Y, Kalbasi R, Karimipour A, Sharifpur M, Meyer J. Using of artificial neural networks (ANNs) to predict the rheological behavior of magnesium oxide-water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates. Math Methods Appl Sci. 2020. https://doi.org/10.1002/mma.6418.

    Article  Google Scholar 

  27. Hemmat Esfe M, Kiannejad Amiri M, Alirezaie A. Thermal conductivity of a hybrid nanofluid. J Therm Anal Calorim. 2018;134:1113–22.

    Article  CAS  Google Scholar 

  28. Hemmat Esfe M, Esfandeh S, Saedodin S, Rostamian H. Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications. Appl Therm Eng. 2017;125:673–85.

    Article  CAS  Google Scholar 

  29. Asadi A, Asadi M, Rezaniakolaei A, Rosendahl LA, Afrand M, Wongwises S. Heat transfer efficiency of Al2O3-MWCNT/thermal oil hybrid nanofluid as a cooling fluid in thermal and energy management applications: an experimental and theoretical investigation. Int J Heat Mass Transf. 2018;117:474–86.

    Article  CAS  Google Scholar 

  30. Afrand M. Experimental study on thermal conductivity of ethylene glycol containing hybrid nano-additives and development of a new correlation. Appl Therm Eng. 2017;110:1111–9.

    Article  CAS  Google Scholar 

  31. Safi M, Ghozatloo A, Shariaty NM, Hamidi A. Preparation of MWNT/TiO2 nanofluids and study of its thermal conductivity and stability; 2014.

  32. Nine MJ, Batmunkh M, Kim J-H, Chung H-S, Jeong H-M. Investigation of Al2O3-MWCNTs hybrid dispersion in water and their thermal characterization. J Nanosci Nanotechnol. 2012;12:4553–9.

    Article  CAS  PubMed  Google Scholar 

  33. Baghbanzadeh M, Rashidi A, Rashtchian D, Lotfi R, Amrollahi A. Synthesis of spherical silica/multiwall carbon nanotubes hybrid nanostructures and investigation of thermal conductivity of related nanofluids. Thermochim Acta. 2012;549:87–94.

    Article  CAS  Google Scholar 

  34. Kumar MS, Vasu V, Gopal AV. Thermal conductivity and rheological studies for Cu–Zn hybrid nanofluids with various basefluids. J Taiwan Inst Chem Eng. 2016;66:321–7.

    Article  CAS  Google Scholar 

  35. Tian M-W, Yan S-R, Tian X-X, Nojavan S, Jermsittiparsert K. Risk and profit-based bidding and offering strategies for pumped hydro storage in the energy market.J Clean Prod 2020;256:120715.

    Article  Google Scholar 

  36. Tian M-W, Yan S-R, Tian X-X, Kazemi M, Nojavan S, Jermsittiparsert K. Risk-involved stochastic scheduling of plug-in electric vehicles aggregator in day-ahead and reserve markets using downside risk constraints method. Sustain Cities Soc 2020;55:102051.

    Article  Google Scholar 

  37. Tian M, Wang L, Yan S, Tian X, Liu Z, Rodrigues JJPC. Research on financial technology innovation and application based on 5G network. IEEE Access. 2019;7:138614–23.

    Article  Google Scholar 

  38. Tian M-w, Yan S, Tian X. Discrete approximate iterative method for fuzzy investment portfolio based on transaction cost threshold constraint. Open Phys. 2019;17(1):41–7.

    Article  Google Scholar 

  39. Müller RH, Hildebrand GE. Zetapotential und Partikelladung in der Laborpraxis(Einführung in die Theorie praktische Messdurchführung Dateninterpretation), Paperback APV 1996.

  40. Ashrae AHF, Atlanta G. American society of Heating, Refrigerating and Air-Conditioning Engineers, 2009.

  41. Bejan A. Convection heat transfer. New York: Wiley; 2013.

    Book  Google Scholar 

  42. Akinshilo AT, Ilegbusi A, Ali HM, Surajo A-J. Heat transfer analysis of nanofluid flow with porous medium through Jeffery Hamel diverging/converging channel. J Appl Comput Mech. 2020;6:433–44.

    Google Scholar 

  43. Patra A, Nayak MK, Misra A. Effects of non-uniform suction, heat generation/absorption and chemical reaction with activation energy on MHD Falkner-Skan flow of tangent hyperbolic nanofluid over a stretching/shrinking Eedge. J Appl Comput Mech. 2020;6:640–52.

    Google Scholar 

  44. Yadav D. The density-driven nanofluid convection in an anisotropic porous medium layer with rotation and variable gravity field: a numerical investigation. J Appl Comput Mech. 2020;6:699–712.

    Google Scholar 

  45. Mondal H, Mishra S, Kundu PK, Sibanda P. Entropy generation of variable viscosity and thermal radiation on magneto nanofluid flow with dusty fluid. J Appl Comput Mech. 2020;6:171–82.

    Google Scholar 

  46. Fallah B, Dinarvand S, Eftekhari Yazdi M, Rostami MN, Pop I. MHD flow and heat transfer of SiC-TiO2/DO hybrid nanofluid due to a permeable spinning disk by a novel algorithm. J Appl Comput Mech. 2019;5:976–88.

    Google Scholar 

  47. Jiang Y, Sulgani MT, Ranjbarzadeh R, Karimipour A, Nguyen TK. Hybrid GMDH-type neural network to predict fluid surface tension, shear stress, dynamic viscosity & sensitivity analysis based on empirical data of iron(II) oxide nanoparticles in light crude oil mixture. Physica A. 2019;526:120948.

    Article  CAS  Google Scholar 

  48. Al-Rashed AA, Ranjbarzadeh R, Aghakhani S, Soltanimehr M, Afrand M, Nguyen TK. Entropy generation of boehmite alumina nanofluid flow through a minichannel heat exchanger considering nanoparticle shape effect. Physica A. 2019;521:724–36.

    Article  CAS  Google Scholar 

  49. Izadi F, Ranjbarzadeh R, Kalbasi R, Afrand M. A new experimental correlation for non-Newtonian behavior of COOH-DWCNTs/antifreeze nanofluid. Physica E. 2018;98:83–9.

    Article  CAS  Google Scholar 

  50. Ranjbarzadeh R, Akhgar A, Musivand S, Afrand M. Effects of graphene oxide-silicon oxide hybrid nanomaterials on rheological behavior of water at various time durations and temperatures: synthesis, preparation and stability. Powder Technol. 2018;335:375–87.

    Article  CAS  Google Scholar 

  51. Ranjbarzadeh R, Isfahani AM, Afrand M, Karimipour A, Hojaji M. An experimental study on heat transfer and pressure drop of water/graphene oxide nanofluid in a copper tube under air cross-flow: applicable as a heat exchanger. Appl Therm Eng. 2017;125:69–79.

    Article  CAS  Google Scholar 

  52. Ranjbarzadeh R, Karimipour A, Afrand M, Isfahani AHM, Shirneshan A. Empirical analysis of heat transfer and friction factor of water/graphene oxide nanofluid flow in turbulent regime through an isothermal pipe. Appl Therm Eng. 2017;126:538–47.

    Article  CAS  Google Scholar 

  53. Ranjbarzadeh R, Moradikazerouni A, Bakhtiari R, Asadi A, Afrand M. An experimental study on stability and thermal conductivity of water/silica nanofluid: eco-friendly production of nanoparticles. J Clean Prod. 2019;206:1089–100.

    Article  CAS  Google Scholar 

  54. Tian M-W, Ebadi AG, Jermsittiparsert K, Kadyrov M, Ponomarev A, Javanshir N, Nojavan S. Risk-based stochastic scheduling of energy hub system in the presence of heating network and thermal energy management. Appl Therm Eng. 2019;159:113825.

    Article  Google Scholar 

  55. Tian M-W, Parikhani T, Jermsittiparsert K, Ashraf MA. Exergoeconomic optimization of a new double-flash geothermal-based combined cooling and power (CCP) system at two different cooling temperatures assisted by boosters. J Clean Prod. 2020;261:120921.

    Article  Google Scholar 

  56. Tian M-W, Yan S-R, Han S-Z, Nojavan S, Jermsittiparsert K, Razmjooy N. New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. J Clean Prod. 2020;249:119414.

    Article  CAS  Google Scholar 

  57. Tian M-W, Yuen H-C, Yan S-R, Huang W-L. The multiple selections of fostering applications of hydrogen energy by integrating economic and industrial evaluation of different regions. Int J Hydrogen Energy. 2019;44:29390–8.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Key project of the National Social Science Foundation of the year 2018 (18AJY013); the 2017 National Social Science foundation project (17CJY072); the 2018 planning project of philosophy and social science of Zhejiang province (18NDJC086YB); the 2018 Fujian Social Science Planning Project (FJ2018B067); The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019 (19YJA790102).

Author information

Authors and Affiliations

  1. Institute of Applied Economics, Yango University, Fuzhou 350015, China

    Shu-Rong Yan

  2. Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Rasool Kalbasi

  3. Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam

    Aliakbar Karimipour

  4. Laboratory of Magnetism and Magnetic Materials, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Masoud Afrand

  5. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Masoud Afrand

Authors
  1. Shu-Rong Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rasool Kalbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aliakbar Karimipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masoud Afrand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoud Afrand.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, SR., Kalbasi, R., Karimipour, A. et al. Improving the thermal conductivity of paraffin by incorporating MWCNTs nanoparticles. J Therm Anal Calorim 145, 2809–2816 (2021). https://doi.org/10.1007/s10973-020-09819-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-020-09819-0

Keywords

Search

Navigation