Lynn S. Bennethum

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We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather prediction , on-site data acquisition using sensors that can survive a passing fire, and on-line(More)
A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used(More)
We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space.(More)
ii To m y father, Evert Vermeer iii ACKNOWLEDGMENTS I w ould rst of all like to thank my advisor, Professor Christoph M. Hoomann, for his guidance and encouragement throughout my y ears in the Computer Science Department. His professional conduct and unnagging support of me and my w ork has provided an excellent model for me to follow i n m y career.(More)
In this dissertation we focus on three related areas of research: 1) principal angles (sometimes denoted as canonical angles) between subspaces including theory and numerical results, 2) Rayleigh quotient and Rayleigh Ritz perturbations and eigenvalue accuracy, and 3) parallel software implementation and numerical results concerning the eigenvalue solver(More)
A systematic development of the macroscopic field equations (conservation of mass, linear and angular momentum, energy, and Maxwell's equations) for a multiphase, multicomponent medium is presented. It is assumed that speeds involved are much slower than the speed of light and that the magnitude of the electric field significantly dominates over the(More)
We describe three different dynamic data-driven applications systems (DDDAS): an empty house, a contaminant identification and tracking, and a wildland fire. Each has something in common with all of the rest and can use some common tools. Each DDDAS is quite complicated in comparison to a traditional static input simulation that is run with large numbers of(More)