Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation |
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| Affiliation: | 1. Young Researchers and Elite Club, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran;2. Department of Chemical Engineering, Faculty of Technology and Engineering, Arak University, Iran;3. Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran;4. Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran;5. Department of Computer Engineering, Imam Hossein University, Tehran, Iran |
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| Abstract: | An accurate artificial neural network (ANN) model and new correlation are developed to predict thermal conductivity of functionalized carbon nanotubes (MWNT-10 nm in diameter)-water nanofluid based on experimental data. Experimental values of thermal conductivity are in six concentrations of nanoparticles from 0.005% up to 1.5%. The temperatures were changed within 10–60 °C. In order to estimate the thermal conductivity, a feed-forward three-layer neural network is utilized. The obtained results exhibited that the new correlation and ANN model have a good agreement with the experimental data. The maximum values of deviation and mean square error of neural network outputs were 2% and 8.2E − 05, respectively. The findings illustrated that the artificial neural network can estimate and model the thermal conductivity of CNTs-water nanofluid very efficiently and accurately. |
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