A well-trained artificial neural network (ANN) using the trainlm algorithm for predicting the rheological behavior of water – Ethylene glycol/WO3 – MWCNTs nanofluid. (February 2022) - Fan, Guangli; A.S., El-Shafay; Eftekhari, S. Ali; Hekmatifar, Maboud; Toghraie, Davood; Mohammed, Amin Salih; Khan, Afrasyab
Abstract: In this study, the influence of volume fraction of nanoparticle ( φ ) and temperatures on the dynamic viscosity ( μ nf ) of water – ethylene glycol/WO3 – MWCNTs hybrid nanofluid was analyzed. For this reason, the μ nf of water – ethylene glycol/WO3 – MWCNTs nanofluid has derived for 42 various experiments through a series of experimental tests, including a combination of 7 different φ 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 φ ) and one output ( μ 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 μ 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 μ nf in every combination of temperature and φ . 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 μ nf . Also, the temperature has an inverse effect on the output parameter ( μ nf ). By increasing the temperature, the μ nf decreases for all φ . At the same time, this decrement is more noticeable at higher φ . For example, they increase the temperature from 25 to 50 °C changes the dynamic viscosity of the pure fluid by only about 15%. In contrast, the same temperature changes in φ = 0.6% cause a 35% drop in μ nf .