Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN)

• Accurate prediction of nanofluid relative viscosity by ANN
• Examination of different network structures in order to find appropriate ANN
• Nanoparticle density as new model input to cover different nanofluids
• Comparison of ANN predictions with different models

Viscosity is a significant physical property of nanofluids in practical heat transfer applications. No general model is capable for accurate prediction of nanofluid viscosity in a broad range of effective parameters. In the present work, 1490 experimental data points on relative viscosity of different nanofluids have been collected by comprehensive literature search. Then, a feed-forward back-propagation multilayer perceptron artificial neural network was developed and tested via employing Levenberg–Marquardt training algorithm in order to predict nanofluid viscosity in broad ranges of operating parameters. The model input parameters are temperature, nanoparticle size, density, volume fraction, and base fluid viscosity. Regression statistical analysis results considering training and test data (R2 = 0.99998), comparison of the ANN predicted values with corresponding experimental data (average absolute relative deviation (AARD) = 0.41%, and maximum average relative deviation (ARD) = 6.44 %) and some available theoretical models have revealed high prediction capability of the developed neural network.