Virtual Telemetry for Dynamic Data-Driven Application Simulations

Craig C. Douglas M.S. M.Phil. Ph.D.

June 25, 2003, 1 p.m. BA 9910

We describe a virtual telemetry system that allows us to devise and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? Only classical analysis works (sort of), but Sobolev theory definitely is missing. A summary of DDDAS features and why this is a really neat new field will be included in the talk. Virtual telemetry has the advantage that it is inexpensive to produce from real time simulations and readily transmittable using (sadly, highly modified) open source streaming software. Real telemetry is usually expensive to receive (if it is even available long term), tends to be messy, comes in no particular order, and can be incomplete or erroneous due to transmission problems or sensor malfunction. We will generate multiple streams continuously for extended periods (e.g., months or years): clean data, somewhat error prone data, and quite lossy or inaccurate data. By studying all of the streams at once we will be able to devise DDDAS components useful in predictive contaminant modeling. This is joint with a cast of infinity: Chad Shannon, Yalchin, Efendiev, Richard Ewing, Raytcho Lazarov, Martin J. Cole, Greg Jones, Chris R. Johnson, and Jenny Simpson ... so far.