Forecasting of Convective Heat Transfer Coefficient in Turbulent Flow of Different Nanofluids in Circular Tubes, Using Artificial Neural Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

HEMMAT ESFE M.; Esfandeh S.; Akhoondzadeh m.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: FLUID MECHANICS AND AERODYNAMICS JOURNAL Year:2018 | Volume:7 | Issue:1 Page(s): 1-11

Download Full-Text

Persian Verion

View:

968

Download:

Cites:

Information Journal Paper

Title

Forecasting of Convective Heat Transfer Coefficient in Turbulent Flow of Different Nanofluids in Circular Tubes, Using Artificial Neural Network

Author(s)

HEMMAT ESFE M. | Esfandeh S. | Akhoondzadeh m. | Issue Writer Certificate

Keywords

NanofluidQ1

Turbulent FlowQ2

Nusselt NumberQ1

Artificial Neural NetworkQ2

Abstract

Modeling of turbulent convective heat transfer of Nanofluids in circular tubes with constant temperature and constant heat flux boundary condition have been performed using Artificial Neural Network. 610 sets of data have been gathered using previous investigations and have been used to train neural network (ANN). The investigated nanoparticles are: TiO2, Graphene, SiC, CuO, SiO2, Fe3O4, and Cu. The base fluid for all these Nanofluids is water. The neural network used has 6 inputs, which includes: nanoparticle density, nanoparticle size, nanoparticle volume fraction, flow Re number, type of boundary condition (constant heat flux or constant temperature) and the amount of heat flux or temperature related to these boundary conditions. Also, the output of neural network is Nusselt Number. Comparing our results with previous investigation, showed that the proposed ANN topology are in good agreement. In this study, the proposed topology of R2=0. 9998 have been choosen between 400 examined ones.

Cites

No record.

References

No record.

Cite

APA: Copy

HEMMAT ESFE, M., Esfandeh, S., & Akhoondzadeh, m.. (2018). Forecasting of Convective Heat Transfer Coefficient in Turbulent Flow of Different Nanofluids in Circular Tubes, Using Artificial Neural Network. FLUID MECHANICS AND AERODYNAMICS JOURNAL, 7(1 ), 1-11. SID. https://sid.ir/paper/245571/en

Vancouver: Copy

HEMMAT ESFE M., Esfandeh S., Akhoondzadeh m.. Forecasting of Convective Heat Transfer Coefficient in Turbulent Flow of Different Nanofluids in Circular Tubes, Using Artificial Neural Network. FLUID MECHANICS AND AERODYNAMICS JOURNAL[Internet]. 2018;7(1 ):1-11. Available from: https://sid.ir/paper/245571/en

IEEE: Copy

M. HEMMAT ESFE, S. Esfandeh, and m. Akhoondzadeh, “Forecasting of Convective Heat Transfer Coefficient in Turbulent Flow of Different Nanofluids in Circular Tubes, Using Artificial Neural Network,” FLUID MECHANICS AND AERODYNAMICS JOURNAL, vol. 7, no. 1 , pp. 1–11, 2018, [Online]. Available: https://sid.ir/paper/245571/en

Related Journal Papers