Chunked Bounding Volume Hierarchies for Fast Digital Prototyping Using Volumetric Meshes.

We present a novel approach to using Bounding Volume Hierarchies (BVHs) for collision detection of volumetric meshes for digital prototyping based on accurate simulation. In general, volumetric meshes contain more primitives than surface meshes, which in turn means larger BVHs. To manage these larger BVHs, we propose an algorithm for splitting meshes into smaller chunks with a limited-size BVH each. Limited-height BVHs make guided, all-pairs testing of two chunked meshes well-suited for GPU implementation. This is because the dynamically generated work during BVH traversal becomes bounded. Chunking is simple to implement compared to dynamic load balancing methods and can result in an overall two orders of magnitude speedup on GPUs. This indicates that dynamic load balancing may not be a well suited scheme for the GPU. The overall application timings showed that data transfers were not the bottleneck. Instead, the conversion to and from OpenCL friendly data structures was causing serious performance impediments. Still, a simple OpenMP acceleration of the conversion allowed the GPU solution to beat the CPU solution by 20 percent. We demonstrate our results using rigid and deformable body scenes of varying complexities on a variety of GPUs.

Prediction of solid fraction from powder mixtures based on single component compression analysis.

The aim of this study was to provide a systematic evaluation of various compression models (Percolation, Kawakita, Exponential model) in respect to predict tablet́s solid fraction for direct compression mixtures, based on single component compression analysis. Four mixtures were compressed over a wide pressure range at various fractions of microcrystalline cellulose (MCC) and pre-agglomerated lactose monohydrate (LAC) to compare an adjusted Percolation, Kawakita and a simple Exponential model. Based on single compression analysis of the pure excipients and application of these models, it was possible to predict the solid fraction of all mixtures. The Kawakita model showed overall superior prediction accuracy, whereas the Percolation model resulted in the best fit for mixtures containing microcrystalline cellulose in a range of 72%-48%. Both models were in good agreement at residuals below 3%.