Monday November 12, 2007

James Leylek, Executive Director of the Clemson University Computational Center for Mobility Systems, spoke at the HPC Consortium meeting about the computational requirements for simulation of vehicle-related phenomena.

A main point of Dr. Leylek's talk was that unsteady simulations are required to adequately model the physical behavior of mobility systems. There are many cases in which unsteady or turbulent mechanisms dominate in this class of problems. There are boundary layer issues, laminar to turbulent transitions, so-called Type II transient flows, etc. They key, though, is finding appropriate numeric techniques to perform these simulations.

Typical mobility application areas include formula-1 race cars, airplane wing design, engine fan design, aircraft carriers, submarines, engine block cooling, and blood flow through artificial hearts.

As an example of the problems sizes in this space, Leylek described what is required to simulate the aerodynamics of a Formula-1 race car. It requires 300M finite element volumes, with eight equations per volume for a total of about 2.4B equations to be solved. And because of the unsteady nature of flows around these bodies, the simulations must be run for tens of thousands of time steps. This essentially means that dedicating even 100 TFLOPs to one team would not be sufficient to allow the dozens of "what if" experiments needed during the vehicle design phase. When one realizes that aerodynamics is just one of a number of attributes that must be simulated for this one application area, the situation becomes even more daunting.

There are a number of numerical methods that can be used to perform these simulations. Full unsteady simulation is impractical for the time being until much larger computational facilities are available at a more affordable cost. In the meantime, what to do? The Computational Center for Mobility Systems at Clemson brings together a large amount of Sun HPC gear and the algorithmic expertise to team with companies and other organizations to perform these simulations using the unique capabilities of semi-deterministic stress model (SDSM) techniques to deliver value to their partners in the shorter term. The point is to be smarter about how these problems should be solved and not be intimidated by the computational requirements predicted by extrapolations based on brute-force methodologies.