Predicting effective thermal conductivity in sands using an artificial neural network with multiscale microstructural parameters

Abstract

Accurate and efficient prediction of thermal conductivity of sands is challenging due to the variations in particle size, shape, connectivity and mineral compositions, and external conditions. Artificial Neural Networks (ANN) models have been used to predict the effective thermal conductivity but they have not considered variables related to particle connectivity. This work uses computed tomography (CT) scanned images of four dry sands and network analysis to redress this significant shortcoming. Here sands are represented as networks of nodes (grains) and edges (interparticle contacts or/and small gaps between neighbouring particles) to extract network features that characterise interparticle connectivity. A network feature – weighted coordination number (WCN) capturing both particle connectivity and contact area – was found to be a good predictor of effective thermal conductivity in dry materials. Roundness, sphericity, solid particle thermal conductivity and porosity are other input parameters rigorously selected for an ANN model that predicts well the effective thermal conductivity of sands.