Artificial Neural Network Model for Thermal Conductivity Estimation of Metal Oxide Water-Based Nanofluids

The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids. Researchers rely on experimental investigations to explore nanofluid properties, as it is a necessary step before their practical application. As these investigations are time and resource-consuming undertakings, an effective prediction model can significantly improve the efficiency of research operations. In this work, an Artificial Neural Network (ANN) model is developed to predict the thermal conductivity of metal oxide water-based nanofluid. For this, a comprehensive set of 691 data points was collected from the literature. This dataset is split into training (70%), validation (15%), and testing (15%) and used to train the ANN model. The developed model is a backpropagation artificial neural network with a 4–12–1 architecture. The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence. It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.