| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Developing Granular Dielectrics Based on Reconstructed Micro-CT Images |
| Author(s) |
Kevin Hager, Christina Wildfire, Edward Sabolsky, Terence Musho |
| On-Site Speaker (Planned) |
Kevin Hager |
| Abstract Scope |
This research is focused on developing statistically equivalent finite element (FE) geometries of granular dielectrics from 3D micro-CT scans. The reconstructed geometries were being used in an electromagnetic FE solver to predict and develop new granular dielectrics. In this study, the dielectric material of interest was a coal char based material. The approach taken involves determining the particle statistics using ImageJ based on 3D micro-CT scans, reconstructing the geometry using a discrete element method (DEM), post-processing in Paraview, and exporting as a CAD neutral file to COMSOL. Particle statistics of interest include statistical distribution of Feret diameter and particle count throughout the entire stack of CT images. The DEM was used to provide a realistic deposition of the particles, where the volume fraction and packing of particles influence the effective dielectric properties. |