Journal article

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

Wenbin Fei, Guillermo A Narsilio, Mahdi M Disfani

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER | PERGAMON-ELSEVIER SCIENCE LTD | Published : 2021

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 interpartic..

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Grants

Awarded by ARC


Funding Acknowledgements

This research was undertaken in the Imaging and Medical Beam Line (IMBL) at the Australian Synchrotron, Victoria, Australia. The authors would like to acknowledge Dr Anton Maksimenko and the other beam scientists at Australian Synchrotron for their support during our experiments. The authors also thank Dr Tabassom Afshar, Dr Joost van der Linden and Dr Xiuxiu Miao for their support in collecting the CT images and thank Gabrielle E. Abelskamp for proofreading the paper. The ARC DP210100433 project provides the basis for this work. The first author thanks The University of Melbourne for offering the Melbourne Research Scholarship.