Presented by: 
Professor Volker Schmidt (Ulm University)
Date: 
Wed 22 Feb, 2:00 pm - 3:00 pm
Venue: 
T105 in Hawken (50)

Model-based 3D simulation of tomographic image data, with applications to virtual materials design

Mathematical models from stochastic geometry can greatly facilitate the quantitative analysis of morphological microstructures of materials used, e.g., in lithium-ion batteries, fuel cells, and polymer solar cells. Not only do they provide a quantitative description of complex microstructures in existing materials, but they also offer the opportunity to construct new virtual morphologies with improved physical properties, using model-based computer simulations. The latter can be achieved by modifying the model parameters and combining the stochastic microstructure models with numerical transport models. In this talk, we present a new approach to stochastic simulation of 3D images, which show complex microstructures reconstructed from electron or synchrotron tomography. Using a multiscale approach, it is possible to decompose complex microstructures into several (less complex) components. In particular, a macroscale component is determined by morphological smoothing, which can be represented by unions of overlapping spheres. This leads to an enormous reduction of complexity and allows us to model the macroscale component by random marked point processes, which is one of the most fundamental classes of models in stochastic geometry.