Skip to main content
SpringerLink
Log in

Numerical investigation of a nanofluidic heat exchanger by employing computational fluid dynamic

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

Abstract

Shell-and-tube heat exchanger is widely applied in different industrial processes and energy systems. Utilized fluid in the heat exchanger and its thermophysical properties are among the key factors that influence the thermal performance and fluid flow in heat exchangers. Employing nanofluids, with enhanced thermal properties, can improve heat transfer rate of heat exchanger. In this regard, the current article concentrates on the numerical simulation of a shell-and-tube heat exchanger with baffle, utilizing multi-walled carbon nanotube/water nanofluid. In this regard, computational fluid dynamic is used for modeling. The applied turbulence model in this study is k-\(\varepsilon\) which is selected based on the literature review. Heat transfer rate comparison in cases of utilizing the nanofluid in shell side of heat exchanger revealed high potential of the nanofluid in thermal performance improvement. Moreover, it was noticed that by using higher concentration of the nanofluid, more enhancement would be obtained which is owing to higher increment in the nanofluid effective thermal conductivity. Average increment in the heat transfer rate of the HE in case of using multi-walled carbon nanotube/water with 4% concentration is around 29.5% in comparison with the condition water is used as the operating fluid.

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

Similar content being viewed by others

References

  1. Erdogan A, Colpan CO, Cakici DM. Thermal design and analysis of a shell and tube heat exchanger integrating a geothermal based organic Rankine cycle and parabolic trough solar collectors. Renew Energy. 2017;109:372–91. https://doi.org/10.1016/j.renene.2017.03.037.

    Article  CAS  Google Scholar 

  2. Ouellette D, Erdogan A, Colpan CO. CFD analysis of a solar-geothermal shell and tube heat exchanger. In: Dincer I, Colpan CO, Kizilkan O, editors. Exergetic, energetic and environmental dimensions. Amsterdam: Elsevier Inc.; 2018. p. 307–22. https://doi.org/10.1016/B978-0-12-813734-5.00017-2.

    Chapter  Google Scholar 

  3. Nada SA, El-Ghetany HH, Hussein HMS. Performance of a two-phase closed thermosyphon solar collector with a shell and tube heat exchanger. Appl Therm Eng. 2004;24:1959–68. https://doi.org/10.1016/j.applthermaleng.2003.12.015.

    Article  CAS  Google Scholar 

  4. Zhang X, Zhang Y, Liu Z, Liu J. Analysis of heat transfer and flow characteristics in typical cambered ducts. Int J Therm Sci. 2020;150:106226. https://doi.org/10.1016/j.ijthermalsci.2019.106226.

    Article  Google Scholar 

  5. Kallannavar S, Mashyal S, Rajangale M. Effect of tube layout on the performance of shell and tube heat exchangers. Mater Today Proc. 2019;27:263–7. https://doi.org/10.1016/j.matpr.2019.10.151.

    Article  Google Scholar 

  6. Mohammadi MH, Abbasi HR, Yavarinasab A, Pourrahmani H. Thermal optimization of shell and tube heat exchanger using porous baffles. Appl Therm Eng. 2020;170:115005. https://doi.org/10.1016/j.applthermaleng.2020.115005.

    Article  Google Scholar 

  7. Rostamian SH, Biglari M, Saedodin S, Hemmat EM. An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation. J Mol Liq. 2017;231:364–9. https://doi.org/10.1016/J.MOLLIQ.2017.02.015.

    Article  CAS  Google Scholar 

  8. Hemmat Esfe M, Rostamian H, Toghraie D, Yan W-M. Using artificial neural network to predict thermal conductivity of ethylene glycol with alumina nanoparticle. J Therm Anal Calorim. 2016;126:643–8. https://doi.org/10.1007/s10973-016-5506-7.

    Article  CAS  Google Scholar 

  9. Hemmat Esfe M, Kiannejad Amiri M, Alirezaie A. Thermal conductivity of a hybrid nanofluid. J Therm Anal Calorim. 2018. https://doi.org/10.1007/s10973-017-6836-9.

    Article  Google Scholar 

  10. Wang P, Li JB, Bai FW, Liu DY, Xu C, Zhao L, et al. Experimental and theoretical evaluation on the thermal performance of a windowed volumetric solar receiver. Energy. 2017;119:652–61. https://doi.org/10.1016/j.energy.2016.11.024.

    Article  CAS  Google Scholar 

  11. Fares M, Al-Mayyahi M, Al-Saad M. Heat transfer analysis of a shell and tube heat exchanger operated with graphene nanofluids. Case Stud Therm Eng. 2020;18:100584. https://doi.org/10.1016/j.csite.2020.100584.

    Article  Google Scholar 

  12. Said Z, Rahman SMA, El Haj AM, Alami AH. Heat transfer enhancement and life cycle analysis of a shell-and-tube heat exchanger using stable CuO/water nanofluid. Sustain Energy Technol Assess. 2019;31:306–17. https://doi.org/10.1016/J.SETA.2018.12.020.

    Article  Google Scholar 

  13. Hemmat EM. Designing a neural network for predicting the heat transfer and pressure drop characteristics of Ag/water nanofluids in a heat exchanger. Appl Therm Eng. 2017;126:559–65. https://doi.org/10.1016/j.applthermaleng.2017.06.046.

    Article  CAS  Google Scholar 

  14. Baghban A, Kahani M, Nazari MA, Ahmadi MH, Yan W-M. Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. Int J Heat Mass Transf. 2019;128:825–35. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2018.09.041.

    Article  CAS  Google Scholar 

  15. Ghalandari M, Mirzadeh Koohshahi E, Mohamadian F, Shamshirband S, Chau KW. Numerical simulation of nanofluid flow inside a root canal. Eng Appl Comput Fluid Mech. 2019;13:254–64. https://doi.org/10.1080/19942060.2019.1578696.

    Article  Google Scholar 

  16. Pakatchian MR, Saeidi H, Ziamolki A. CFD-based blade shape optimization of MGT-70(3)axial flow compressor. Int J Numer Methods Heat Fluid Flow. 2019. https://doi.org/10.1108/HFF-10-2018-0603.

    Article  Google Scholar 

  17. Farhadi-Azar R, Ramezanizadeh M, Taeibi-Rahni M, Salimi M. Compound triple jets film cooling improvements via velocity and density ratios: large Eddy simulation. J Fluids Eng. 2011;133:031202. https://doi.org/10.1115/1.4003589.

    Article  Google Scholar 

  18. Wang Y, Kamari ML, Haghighat S, Ngo PTT. Electrical and thermal analyses of solar PV module by considering realistic working conditions. J Therm Anal Calorim. 2020. https://doi.org/10.1007/s10973-020-09752-2.

    Article  Google Scholar 

  19. Pal E, Kumar I, Joshi JB, Maheshwari NK. CFD simulations of shell-side flow in a shell-and-tube type heat exchanger with and without baffles. Chem Eng Sci. 2016;143:314–40. https://doi.org/10.1016/j.ces.2016.01.011.

    Article  CAS  Google Scholar 

  20. Somasekhar K, Malleswara Rao KND, Sankararao V, Mohammed R, Veerendra M, Venkateswararao T. A CFD investigation of heat transfer enhancement of shell and tube heat exchanger using Al2O3–water nanofluid. Mater Today Proc. 2018;5:1057–62. https://doi.org/10.1016/j.matpr.2017.11.182.

    Article  CAS  Google Scholar 

  21. Ambekar AS, Sivakumar R, Anantharaman N, Vivekenandan M. CFD simulation study of shell and tube heat exchangers with different baffle segment configurations. Appl Therm Eng. 2016;108:999–1007. https://doi.org/10.1016/j.applthermaleng.2016.08.013.

    Article  Google Scholar 

  22. Ghalandari M, Maleki A, Haghighi A, Safdari Shadloo M, Alhuyi Nazari M, Tlili I. Applications of nanofluids containing carbon nanotubes in solar energy systems: a review. J Mol Liq. 2020;313:113476. https://doi.org/10.1016/j.molliq.2020.113476.

    Article  CAS  Google Scholar 

  23. Hwang YJ, Ahn YC, Shin HS, Lee CG, Kim GT, Park HS, et al. Investigation on characteristics of thermal conductivity enhancement of nanofluids. Curr Appl Phys. 2006;6:1068–71. https://doi.org/10.1016/j.cap.2005.07.021.

    Article  Google Scholar 

  24. Garg P, Alvarado JL, Marsh C, Carlson TA, Kessler DA, Annamalai K. An experimental study on the effect of ultrasonication on viscosity and heat transfer performance of multi-wall carbon nanotube-based aqueous nanofluids. Int J Heat Mass Transf. 2009;52:5090–101. https://doi.org/10.1016/j.ijheatmasstransfer.2009.04.029.

    Article  CAS  Google Scholar 

  25. Xie H, Lee H, Youn W, Choi M. Nanofluids containing multiwalled carbon nanotubes and their enhanced thermal conductivities. J Appl Phys. 2003;94:4967–71. https://doi.org/10.1063/1.1613374.

    Article  CAS  Google Scholar 

  26. Wen D, Ding Y. Effective thermal conductivity of aqueous suspensions of carbon nanotubes (carbon nanotube nanofluids). J Thermophys Heat Transf. 2004;18:481–5. https://doi.org/10.2514/1.9934.

    Article  CAS  Google Scholar 

  27. Venkata Sastry NN, Bhunia A, Sundararajan T, Das SK. Predicting the effective thermal conductivity of carbon nanotube based nanofluids. Nanotechnology. 2008;19:055704. https://doi.org/10.1088/0957-4484/19/05/055704.

    Article  PubMed  CAS  Google Scholar 

  28. Xie H, Chen L. Adjustable thermal conductivity in carbon nanotube nanofluids. Phys Lett Sect A Gen At Solid State Phys. 2009;373:1861–4. https://doi.org/10.1016/j.physleta.2009.03.037.

    Article  CAS  Google Scholar 

  29. Nasiri A, Shariaty-Niasar M, Rashidi AM, Khodafarin R. Effect of CNT structures on thermal conductivity and stability of nanofluid. Int J Heat Mass Transf. 2012;55:1529–35. https://doi.org/10.1016/j.ijheatmasstransfer.2011.11.004.

    Article  CAS  Google Scholar 

  30. Phuoc TX, Massoudi M, Chen RH. Viscosity and thermal conductivity of nanofluids containing multi-walled carbon nanotubes stabilized by chitosan. Int J Therm Sci. 2011;50:12–8. https://doi.org/10.1016/j.ijthermalsci.2010.09.008.

    Article  CAS  Google Scholar 

  31. Sadri R, Ahmadi G, Togun H, Dahari M, Kazi SN, Sadeghinezhad E, et al. An experimental study on thermal conductivity and viscosity of nanofluids containing carbon nanotubes. Nanoscale Res Lett. 2014. https://doi.org/10.1186/1556-276X-9-151.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Liu MS, Ching-Cheng Lin M, Te HI, Wang CC. Enhancement of thermal conductivity with carbon nanotube for nanofluids. Int Commun Heat Mass Transf. 2005;32:1202–10. https://doi.org/10.1016/j.icheatmasstransfer.2005.05.005.

    Article  CAS  Google Scholar 

  33. Ozden E, Tari I. Shell side CFD analysis of a small shell-and-tube heat exchanger. Energy Convers Manag. 2010;51:1004–14. https://doi.org/10.1016/j.enconman.2009.12.003.

    Article  CAS  Google Scholar 

  34. Estellé P, Halelfadl S, Maré T. Thermal conductivity of CNT water based nanofluids: experimental trends and models overview. J Therm Eng. 2015;1:381. https://doi.org/10.18186/jte.92293.

    Article  Google Scholar 

  35. Aramesh M, Pourfayaz F, Kasaeian A. Numerical investigation of the nanofluid effects on the heat extraction process of solar ponds in the transient step. Sol Energy. 2017;157:869–79. https://doi.org/10.1016/J.SOLENER.2017.09.011.

    Article  CAS  Google Scholar 

  36. Nazari MA, Ghasempour R, Ahmadi MH, Heydarian G, Shafii MB. Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. Int Commun Heat Mass Transf. 2018;91:90–4. https://doi.org/10.1016/j.icheatmasstransfer.2017.12.006.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, Hubei, China

    Dongtao Hu, Jing Wang & Qi Tang

Authors
  1. Dongtao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongtao Hu.

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

Hu, D., Wang, J. & Tang, Q. Numerical investigation of a nanofluidic heat exchanger by employing computational fluid dynamic. J Therm Anal Calorim 144, 1831–1838 (2021). https://doi.org/10.1007/s10973-020-10355-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-020-10355-0

Keywords

Search

Navigation