Increasing the accuracy of estimating the viscosity of SAE40-based nanofluid containing MWCNT-TiO2 nanoparticles using a creative method in optimizing the structure of the artificial neural network

The viscosity (mu nf) is one of the influencing parameters in choosing that nanofluid (NF) that affects its thermal behavior and heat transfer. In this regard, the present study is conducted to investigate the mu nf of MWCNT-TiO2 (10:90) /SAE40 NF using an artificial neural network (ANN). ANN design in temperature conditions between 25 and 50 degrees C and in different solid volume fraction (SVF) of nanoparticles (phi =0.0625%-1%). 174 laboratory data were used. Three inputs (temperature(T), phi and shear rate(SR)) and one output (mu nf) are determined for ANN. In this study, ANN modeling was done with MLP method and Levenberg-Marquardt (LM) training algorithm. The selected optimal structure, among different ANN structures for MWCNT-TiO2(10:90)/SAE40 NF, has two hidden layers with an optimal structure of 10 and 4 neurons in the first and second layers. The results of R and MSE coefficients prove the accuracy of the proposed model., which in the final stage was set equal to 0.9999507 and 0.004599708, respectively. The margin of deviation (MOD) in the grid data set is in the range 3% <MOD < + 3%. Based on studies that considered ANN correlation with computational data compared to laboratory data, the results of this comparison show that the ANN is more accurate and reliable for estimating the mu nf. The turning point of this article is designing and predicting the viscosity of MWCNT-TiO2/SAE40 NF in the least time and financial costs compared to repetitive and time-consuming experiments through ANN.