PNNL

Abstract

Reconstruction of simulated microstructure from statistical microstructure descriptors attracts strong research interest due to its importance in materials design. A new methodology is presented in this paper to reconstruct robust microstructure with large number of representative volume elements which may acts as a stable input for deterministic method to simulate performance and effective properties. It is applied in carbon nanotube composite to demonstrate the capability of this method to generate robust microstructure while incorporating more statistical information on geometry, shape, anisotropy and spatial arrangement. Not only one point based statistical information, such as size, volume fraction, is taken into consideration, but correlation function is incorporated to cover information from geometry, shape and spatial correlation. Monte Carlo method was applied in reconstruction. Instead of using discrete image matrix, the information of geometric distribution of the nanotube composite is stored with the information of location of nanotubes. In this way, robust micrographs with large number of representative volume elements were generated for the future evaluation using finite element methods.