Increasing the accuracy of estimating the dynamic viscosity of hybrid nano-lubricants containing MWCNT-MgO by optimizing using an artificial neural network

Artificial neural network (ANN) is utilized as efficient models to forecast the nanofluids (NFs) viscosity (mu nf). In this examination, ANN is used to forecast the mu nf of the MWCNT-MgO (25 %-75 %) / SAE40 nano-lubricant (NL) experimental data set. Experimental evaluation of NLs is taken in volume fraction of nanoparticles (NPs) yo = 0.0625 %-1% and temperature range of T = 25 to 50 degrees C. To predict the mu nf of the data using ANN, a multilayer perceptron (MLP) ANN with the algorithm of Levenberg-Marquardt (LM) is utilized. For ANN modeling, temperature, yo and shear rate (_c) are determined as inputs and mu nf is determined as output. From 400 various ANN samples for NL, the optimal sample (OS) is selected, comprising two hidden layers (HLs) with the OS of 8 and 5 neurons in the primary and second layer, respectively. Eventually, for the OS, the amount of the regression coefficient (RC) and the mean square error (MSE) are set equal to 0.9999882 and 0.001453292, respectively. The margin of deviation (MOD) for all ANN information is in the range of less than-1% 1%. It's good because the ANN pattern is more pre-cise and has a great ability to forecast mu nf. The main goal of this research is to model and estimate the mu nf of MWCNT-MgO (25:75)/SAE40 NL through ANN and also to select the optimal structure from the set of predicted ANN structures and manage time and cost.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).