Forecasting the thermal conductivity of a nanofluid using artificial neural networks
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Forecasting the thermal conductivity of a nanofluid using artificial neural networks Sara Rostami1,2 · Rasool Kalbasi3 · Nima Sina3 · Aysan Shahsavar Goldanlou4,5 Received: 12 June 2020 / Accepted: 11 August 2020 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract In this study, the influence of incorporating MWCNT on the thermal conductivity of paraffin was evaluated numerically. Input variables including mass fraction (0.005–5%) and temperature (25–70 °C) were introduced as input and nanofluid thermal conductivity was considered as an output parameter. Thermal conductivity was modeled numerically through two techniques. In the first technique, an algorithm is applied to find the best artificial neural network (ANN) and in the second technique, a response surface methodology (RSM) on data points has been implemented. Eventually, the mean square error, correlation coefficient and maximum margin of deviation in both techniques have been compared. Calculations revealed that the ANN containing hidden layer with six neurons has priority over other ANN. The correlation coefficient for ANN and RSM was 0.993 and 0.972 which imply that ANN method has more accuracy than RSM technique. Keywords Nanofluid · RSM · ANN · Paraffin Abbreviations DLS Dynamic lights scattering EG Ethylene glycol K Thermal conductivity W m−1 K−1 R2 R-square 2 Radj Adjusted R-square T Temperature (°C) TCR Thermal conductivity ratio vol.% Volume fraction (%) mass% Mass fraction (%)
* Aysan Shahsavar Goldanlou [email protected] Sara Rostami [email protected] 1
Laboratory of Magnetism and Magnetic Materials, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3
Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
4
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
5
Faculty of Electrical – Electronic Engineering, Duy Tan University, Da Nang 550000, Vietnam
Subscript bf Base fluid nf Nanofluid
Introduction Convection heat transfer is one of the chief physical phenomena that are very important in the industrial sector [1–4]. Because in many parts of the industry, this type of heat transfer is the dominant mode [5]. Three techniques can be suggested to enhance convection heat transfer. In the first technique, by increasing the temperature difference between the solid surface and fluid, the convection heat transfer can be increased [6]. The second technique focuses on increasing the heat transfer area [7–10]. In the last technique, researchers are looking for methods that improve the convection heat transfer coefficient. One of these methodologies is to enhance the fluid thermal conductivity of the using nanomaterials [11, 12]. Many researchers have focused on the effects of adding nanoparticles in the base fluid [13–15]. The amount of variation in the thermophysical properties is a function of para
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