Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network
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https://doi.org/10.1007/s11630-019-1158-9
Article ID: 1003-2169(2019)00-0000-00
Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network WANG Xuehui1, YAN Xiaona2*, GAO Neng3, CHEN Guangming3 1. Fluids and Thermal Engineering Research Group, Faculty of Engineering, University of Nottingham, NG7 2RD, UK 2. School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China 3. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China © Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract: The nanofluid has been widely used in many heat transfer areas due to its significant enhancement effect on the thermal conductivity. Therefore, the methods that can accurately predict their thermal conductivities are very important to evaluate and analyze the heat transfer process. In this paper, a novel artificial neural network (ANN) model was proposed to predict the thermal conductivity of nanofluids with ethylene glycol and could be used in a wide range with excellent accuracy. A total of 391 experimental data with a wide range of temperatures (4°C~90°C), nanoparticles (metal, metal oxide, etc.), volume concentrations (0.05%~10%), and particle sizes (2 nm ~ 282 nm) were collected. To build the ANN model, the temperature, thermal conductivities of the base fluid and nanoparticles, the size and volume concentration of the nanoparticles were selected and used as the input parameters. There were 5 nodes, 10 nodes and 1 node in input layer, hidden layer and output layer, respectively. The predicted results of the ANN model coincided with the experimental data very well with the correlation coefficient and mean square error (MSE) were 0.9863 and 3.01×10-5, respectively. The relative deviations of 99.74% data were within ±5%. The model was expected to be a good practical method to predict the thermal conductivity of nanofluids with ethylene glycol.
Keywords: thermal conductivity, nanofluids, ANN model, heat transfer
1. Introduction With the rapid development of the technologies, the requirement on heat transfer process is getting higher and higher and poses great challenges to the thermal devices in many areas [1-3]. A lot of attention has been paid to search for more efficient ways to dissipate the heat. As an important concept, improving the thermo-physical properties of the working fluid is expected to be a significant way to enhance the heat transfer process [4]. Against this context, the nanofluid was introduced in the Received: Apr 10, 2019
late 1990s by Choi [5]. The nanofluids are made by adding some solid nanoparticles to the base fluid [6]. The nanoparticles can be metal (such as Cu, Al, Fe), metal oxide (such as CuO, Al2O3, MgO), carbonic (such as SiC, CNT, graphite) [7], etc. The water and ethylene glycol are most commonly used as the base fluids. Considering the thermal conductivities of these solid materials are generally greater than the fluids, so the add
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