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• W2214851944 abstract "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." @default.
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• W2214851944 date "2016-08-01" @default.
• W2214851944 modified "2023-09-26" @default.
• W2214851944 title "Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation" @default.
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• W2214851944 doi "https://doi.org/10.1016/j.icheatmasstransfer.2015.12.012" @default.
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