Title: Large Scale Frictional Contact Dynamics on the GPU

Abstract:
In the context of simulating the dynamics of large systems ofinteracting rigid bodies, this talk summarizes a method for solving large cone complementarity problems by means of a fixed-point iteration algorithm. The method is an extension of the Gauss-Jacobi algorithms with over-relaxation for symmetric convex complementarity problems. Convergent under fairly standard assumptions, the method is implemented in a scalable parallel computational framework by using a single instruction multiple data (SIMD) execution paradigm supported by the Compute Unified Device Architecture (CUDA) library for programming on the graphical processing unit (GPU). The simulation framework developed supports the analysis of problems with more than one million rigid bodies that interact through contact and friction forces, and whose dynamics are constrained by either unilateral or bilateral kinematic constraints. Simulation thus becomes a viable tool for investigating in the near future the dynamics of complex systems such as the Mars Rover operating on granular terrain, powder composites, and granular material flow.

Short Bio:
Dan Negrut received his Mechanical Engineering Ph.D. in 1998 from the University of Iowa, working under the supervision of Professor Emeritus Edward J. Haug. He spent six years working for Mechanical Dynamics, Inc., a software company in Ann Arbor, Michigan. In 2004 he served as an Adjunct Assistant Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. He spent 2005 as a Visiting Scientist at Argonne National Laboratory in the Mathematics and Computer Science Division. At the end of 2005 Dan joined the Mechanical Engineering faculty at the University of Wisconsin, Madison. His interests are in Computational Science and he leads the Simulation-Based Engineering Lab at Wisconsin. Ongoing projects focus on large scale multibody dynamics, uncertainty quantification, numerical integration methods for dynamic systems, and reduced order modeling and metamodeling. For his research and educational initiatives Dan received in 2009 a National Science Foundation Career Award.