Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network

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.