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

Abstract

Artificial neural network (ANN) is utilized as efficient models to forecast the nanofluids (NFs) viscosity (μnf). In this examination, ANN is used to forecast the μnf 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) φ= 0.0625 % −1% and temperature range of T = 25 to 50 °C. To predict the μnf of the data using ANN, a multilayer perceptron (MLP) ANN with the algorithm of Levenberg-Marquardt (LM) is utilized. For ANN modeling, temperature, φ and shear rate (γ̇) are determined as inputs and μnf 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% <MOD <+1%. It's good because the ANN pattern is more precise and has a great ability to forecast μnf. The main goal of this research is to model and estimate the μnf 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.

Keywords

• ANN
• Experimental data
• Hybrid nano-lubricants
• Dynamic viscosity
• nanolubricant
• Levenberg-Marquardt