Computational Physics Group
Sharp Volumetric Billboard Based Characterization and Modeling of Complex 3D Ni/Al High Energy Ball Milled Composites
Dewen Yushu1, Sangmin Lee2 and K.
1Department of Aerospace and Mechanical Engineering
2Center for Shock Wave-processing of Advanced Reactive Materials,
University of Notre Dame
Notre Dame, IN, 46556, USA.
We present an innovative image-based modeling technique, based on Google Earth like algorithms, to effectively resolve intricate material morphology and address the computational complexity associated with heterogeneous materials. This sharp volumetric billboard algorithm stems from a volumetric billboard method, a multi-resolution modeling strategy in computer graphics. In this work, we enhance volumetric billboards through a sharpening filter to reconstruct the statistical information of heterogeneous systems. A hierarchy of microstructures is created for high energy ball milled Ni/Al composites. We analyze the first- and second-order statistics of microstructures, and characterize both macro- and micro-mechanical material responses. Furthermore, we conduct a convergence study of the associated computational results. The statistical and mechanical robustness of data compression is demonstrated through the corresponding error analysis.
We propose a novel image-based modeling technique with high-fidelity and a reduced amount of data for the analysis of complex heterogeneous materials. The SVB technique is based on VB -- a Google Earth like method -- that utilizes image compression while retaining important morphological features.
We improve VB through minimizing density-like greyscale value PMFs among microstructure LODs. In particular, we design a filter to reconstruct the statistical properties of higher LODs in order to create a series of SVB microstructures. The material density indicator -- the greyscale value PMF -- is restored through this procedure. According to the statistical characterization, the new SVB method preserves the one- and two-point correlation functions among all analyzed LODs. The statistical and mechanical behavior of SVB microstructures have the potential to efficiently reduce the computational complexity by performing computations on a coarser microstructure instead of on the finest one, while retaining a high-degree of accuracy.
We simulate a tension-relaxation loading process of HEBM Ni/Al composites assuming both a single crystal representation and random textures. We study macro- and micro-mechanical responses using the overall and local stress fields, respectively. Error analysis shows promise of the SVB microstructures in capturing the mechanical behavior. RE confirms the convergence properties of the SVB technique. An analysis of a polycrystal with random texture shows the importance of the crystallography and the need for a proper orientation reduction.
This novel SVB model can be applied to a variety of material systems. It lends itself to data compression and computational analysis with controlled accuracy in computational materials science studies.
This work was supported by the
Department of Energy, National Nuclear Security
Administration, under the award No. DE-NA0002377 as part
of the Predictive Science Academic Alliance Program II.
We would also like to acknowledge computational
resources from the 2016 ASCR Leadership Computing
© 2017 Notre Dame and Dr. Karel Matous