Artificial brain structure-based modeling to predict the photo-thermal conversion performance of graphene nanoplatelets
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Artificial brain structure‑based modeling to predict the photo‑thermal conversion performance of graphene nanoplatelets nanofluid using experimental data M. Yahyaei1 · M. Vakili1 · B. Paknezhad2 Received: 3 April 2020 / Accepted: 19 August 2020 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract Radiation is one of the means of thermal energy and heat transfer. Therefore, investigating and determining materials’ abilities to absorb energy and heat can be beneficial in design process of different thermal systems. In this study, thermal radiation characteristics (e.g., absorption rate and transmittance coefficient) of graphene nanofluids in different wavelengths are predicted using modeling approach based on feedforward neural network with Levenberg–Marquardt backpropagation algorithm as a type of artificial neural network (ANN). For model development, data from experimental measurements of thermal conductivity of nanofluid with graphene nanoplatelet/water deionized in the laboratory were used. Different wavelengths ranging from 200 to 2500 nm and various mass fractions of 0.025, 0.050, 0.075 and 0.100 were considered in developing the model. Overall, 924 data samples were used of which 648 were for ANN training and the rest were for calibration and validation. To ensure high accuracy level of modeling and prediction, statistical indexes such as root-mean-square error, mean absolute percentage error and coefficient of determination (R2), were employed, which indicate values of 0.041, 0.059 and 0.998, respectively. Modeling results and associated statistical indexes based on experimental data demonstrate high accuracy and validity of the ANN modeling compared to other prediction methods. Keywords Artificial neural network · Nanofluid · Graphene nanoplatelet · Modeling · Radiation heat transfer
Introduction Increasing efficiency of thermal systems has always been under investigation and the main focus of many researchers around the world [1]. For the purpose of enhancing thermal system’s efficiency and functionality, aside from common methods such as increasing surface contact and creating turbulence in fluid’s flow, adding metal and non-metal particles to the fluid is one of the best and most efficient methods. This is mainly because thermal conductivity rate of metals and metal-oxides is much higher than fluids such as oil and water [2]. Hence, using nanofluids in thermal systems most
* M. Vakili [email protected] 1
Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Chemical, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2
likely improves thermal transmittance properties of those systems [3]. In other words, since metal particles do have higher thermal conductivity compared to non-metal particles and materials, adding metal particles to fluids and using them in thermal systems and heat absorption-based devices will result in enhanced efficiency [4, 5]. Also, in addition to thermal conductivity coefficient of nanoparticles, there are o
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