Prediction of mass transport properties in 3D microstructures using 2D CNNs
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2022
Författare
Valdimarsson, Sævar Óli
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Porous materials and the relationship between their 3D microstructure and their
mass transport properties is of interest in multiple fields. To analyse this relationship
and build an understanding of it requires a great quantity of data, but obtaining
experimental 3D data is difficult and expensive. An alternative is to generate virtual
microstructures and simulate their mass transports, which can then be used to
estimate the relationship. 2D experimental data is easier to obtain and work with
than 3D experimental data, e.g. it requires less storage space and memory. It is
of interest to investigate models that can estimate mass transport properties of 3D
microstructures from 2D data. In this work, 2D data is extracted from a pre-existing
3D virtual microstructure dataset and the viability of using 2D convolutional neural
networks (CNNs) to predict the mass transport properties is explored.
Keywords:
Beskrivning
Ämne/nyckelord
microstructure, mass transport properties, convolutional neural network, 2D, 3D.