عنوان
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Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation
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نوع پژوهش
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مقاله چاپشده
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کلیدواژهها
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Thermal conductivity, Nanofliud, Thermophysical properties Artificial neural network, Carbon nanotubes
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چکیده
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An accurate artificial neural network (ANN)model and newcorrelation 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 feedforward 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 andmodel the thermal conductivity of CNTs-water nanofluid very efficiently and accurately.
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پژوهشگران
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کاظم مطهری (بازنشسته) (نفر اول)
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