A well-trained artificial neural network (ANN) using the trainlm algorithm for predicting the rheological behavior of water - Ethylene glycol/WO3 - MWCNTs nanofluid

In this study, the influence of volume fraction of nanoparticle (phi) and temperatures on the dynamic viscosity (mu(nf)) of water - ethylene glycol/WO3 - MWCNTs hybrid nanofluid was analyzed. For this reason, the mu(nf) of water - ethylene glycol/WO3 - MWCNTs nanofluid has derived for 42 various experiments through a series of experi-mental tests, including a combination of 7 different phi and 6 various temperatures. These data were then used to train an Artificial Neural Network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward Perceptron ANN with two inputs (T and phi) and one output (mu(nf)) were used. The best topology of the network was determined by trial and error, and two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. Also, to analyze the effect of various training algorithms on the performance of mu(nf) prediction, 10 different training functions were used for this reason, and the best ANN was obtained when the trainbr is used as a training function. The trained ANN roles as a predicting function of mu(nf) in every combination of temperature and phi. The obtained results show that a well-trained ANN is created using the trainlm algorithm and showed an MSE value of 4.2e-4 along 0.998 as a correlation coefficient for predicting mu(nf). Also, the temperature has an inverse effect on the output parameter (mu(nf)). By increasing the temperature, the mu(nf) decreases for all phi. At the same time, this decrement is more noticeable at higher phi. For example, they in-crease the temperature from 25 to 50 degrees C changes the dynamic viscosity of the pure fluid by only about 15%. In contrast, the same temperature changes in phi= 0.6% cause a 35% drop in mu(nf).