Lynn S. Bennethum

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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)
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 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)
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)
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