Modeling Thermal Conductivity Ratio of CuO/Ethylene Glycol Nanofluid by Using Artificial Neural Network

The thermal conductivity of nanofluids depends on several factors such as temperature, concentration, and temperature. These parameters have the most significant effect on thermal conductivity compared with other factors. In the present study, the accuracy of trained Perceptron neural network with 10 neurons and three input variables including size of nanoparticles, temperature, and concentration is evaluated. The sum of squared errors and the correlation coefficient of the trained neural network are equal to 0.99293 and 0.00031, respectively.