A unique artificial intelligence approach and mathematical model to accurately evaluate viscosity and density of several nanofluids from experimental data

Abstract

In the present research paper, a unique single feed-forward multilayer perceptron neural network (MLPNN) based on data transformation technique and mathematical models were proposed for forecasting density and dynamic viscosity of Alumina (Al2O3)/distilled water (DW), multiwall carbon nanotube (MWCNT)/DW, and Graphene nanoparticle (GNP)/DW nanofluids. The density and dynamic viscosity of nanofluids (0.1%, 0.25%, 0.5%, 0.75%, and 1%) were experimentally measured in temperature range 30–80 °C. Nanofluids, volume concentration, and temperature were given as input and density and dynamic viscosity as output from the network. The statistical analysis was performed to predict the accuracy of both models. The experiments revealed that density and viscosity increase with an increase in nanofluid concentration, and decrease with an increase in temperature. The maximum increase in dynamic viscosity and density of 41.59% and 5.06% was noticed for Al2O3/DW nanofluid. The outcome shows that the MLPNN model is well trained at 12 perceptrons in the hidden layer using the Levenberg-Marquardt algorithm. The output obtained from MLPNN and the mathematical model were compared with experimental results. The maximum error < 0.2% in density and < 1% in viscosity measurement was noticed for both models.