Research and Opinion: Reducing Modeling Bias in Statistical Volume Elements

Dr. Katherine Acton, an associate professor of mechanical engineering, recently co-authored “Geometric partitioning schemes to reduce modeling bias in statistical volume elements smaller than the scale of isotropic and homogeneous size limits,” which was published by the Journal of Computer Methods in Applied Mechanics and Engineering. 

From the article: In this work, elastic and strength properties will be evaluated at multiple scales using SVE with square and circular (so-called “regular”) geometry. A Voronoi tessellation based geometry, where aggregation of Voronoi cells is achieved using square or circular grids, is studied and compared with regular partitioning methods. Models will be tested on two types of microstructures, one that is isotropic at the macroscale, and one which contains a slight directional bias. Results for different SVE geometries will be compared to demonstrate how SVE modeling choices affect the characterization of anisotropy and heterogeneity at the micro- and mesoscale.