DIVISION OF ENGINEERING SPECIAL SEMINAR
Dynamic Data-Driven Application Simulations (DDDAS) for Wildfire
Craig Douglas
Departments of Computer Science and Mechanical Engineering,
Center for Computational Sciences, University of Kentucky, Lexington, KY
and
Department of Computer Science, Yale University, New Haven, CT
DDDAS is a new paradigm in which data dynamically controls almost all aspects of long term simulations. Rather than run many simulations using static data as initial conditions, a small number of simulations are run with additional data injected as it becomes available. In practice, the data streaming in may have errors and therefore may not be completely accurate or reliable (for example, in reservoir data sets, a 15% error in the data is common). As a result, the nonlinear equations do not need to be precisely solved at each step. This can expedite the execution.
Using the data appropriately lets the physical and mathematical models, the discretization, and the scales of interesting parts of the computations become parameters that can be changed during the course of the simulation. In addition, error propagation is of particular interest in nonlinear time dependent simulations and an accurate resolution of the error allows an intelligent and theoretically justifiable basis for restarts or for recomputing solutions in the recent past in a subdomain.
In this talk, a DDDAS approach to wildfire simulation will be taken. Details of how to transform a traditional wildfire simulator and model into a DDDAS will be presented. The data for the wildfire simulator comes from sensors on the ground as well as aerial and satellite photography at various wavelengths. We would like to visualize the results on a variety of devices: from an immersive environment in a remote building to a cell phone or PDA on a mountainside. We will use the NSF TeraGrid as an on demand computational resource.
Monday, April 11, 2005
5:00-6:00 p.m.
Barus & Holley 190