کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
652880 | 1457478 | 2016 | 6 صفحه PDF | دانلود رایگان |
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.
Journal: International Communications in Heat and Mass Transfer - Volume 76, August 2016, Pages 376–381