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CFD analysis of heat transfer and friction factor characteristics of nanofluid flow in microtubes

  • Gandhi Marikannan and Perarasu V. Thangavelu ORCID logo EMAIL logo
Published/Copyright: July 14, 2025
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Abstract

Nanofluids (NFs), a new type of nano-engineered colloidal suspension, consist of solid particles at the nanoscale suspended in a base liquid. In many applications, the impact of dispersing nanoparticles (NPs) on the thermal (hydrothermal) and hydraulic performance of traditional coolants is a significant issue. The current paper reports the results of numerical research on the MWCNT (Multi Wall Carbon Nano Tube) nanofluid flowing in a microtube, developing laminar flows under constant heat flux conditions. The numerical investigations are conducted for a Reynolds number range from 200 to 500, and a particle concentrations are 0.075 %, 0.125 %, 0.25 %. The effects of the Reynolds number on the friction factor and heat transfer coefficient in the microtube are computed using OpenFOAM software and compared satisfactorily with the experimental results of the literature. In order to evaluate performance improvements, the governing equations of fluid flow and heat transfer were discretised using the finite volume method, and nanofluid thermophysical properties were added. The results shows that while increasing the MWCNT volume percentage increased the friction factor, it also greatly improved heat transmission through higher thermal conductivity. The predictive accuracy of the model was demonstrated by its error percentages of ±12 % for friction factor predictions and below ±10 % for the heat transfer coefficient. This study confirms OpenFOAM as a useful tool for simulating advanced nanofluid behaviour in constrained flows and demonstrates the potential of MWCNT nanofluids in micro-scale heat transfer systems.


Corresponding author: Perarasu V. Thangavelu, Thermal and Bio-Analysis Laboratory, Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-04-15
Accepted: 2025-06-30
Published Online: 2025-07-14

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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