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Updated: May 30, 2026
05:20
Characterization of Thermal Transport in One-dimensional Solid Materials
Published on: January 26, 2014
Predicting the effective thermal conductivity of carbon nanotube based nanofluids
N N Venkata Sastry1, Avijit Bhunia, T Sundararajan
1Department of Mechanical Engineering, Indian Institute of Technology, Madras, Chennai 600 036, India.
Nanotechnology
|August 6, 2011
Summary
A new model explains how carbon nanotubes (CNTs) enhance liquid thermal conductivity. The model, based on CNT chain formation, accurately predicts experimental data and highlights key factors influencing thermal enhancement in nanofluids.
Area of Science:
- Materials Science
- Nanotechnology
- Fluid Dynamics
Background:
- Carbon nanotubes (CNTs) significantly improve liquid thermal conductivity.
- Experimental data on CNT-based nanofluid thermal enhancement show wide, unexplained variations.
Purpose of the Study:
- To develop a theoretical model for thermal conductivity enhancement in nanofluids.
- To explain the wide range of experimental results for CNT nanofluids.
- To identify key parameters governing thermal conductivity in CNT nanofluids.
Main Methods:
- Developed a model based on 3D carbon nanotube chain formation (percolation) and thermal resistance networks.
- Considered random CNT orientation and CNT-CNT interactions in the model.
- Validated model predictions against available experimental data.
Main Results:
- The model accurately predicts thermal conductivity enhancement across a wide range of experimental data.
- Key factors influencing enhancement include CNT geometry, volume fraction, base liquid conductivity, and preparation method.
- A new dimensionless parameter was introduced to characterize nanofluid thermal conductivity with ~5% accuracy.
Conclusions:
- CNT chain formation (percolation) is the primary mechanism for thermal conductivity enhancement in nanofluids.
- The developed model provides a unified explanation for experimental observations.
- The new dimensionless parameter offers a simplified and accurate method for predicting nanofluid thermal performance.
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