10 research outputs found
A wildland fire model with data assimilation
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 to assimilate
temperatures measured at selected points into running wildfire simulations. The
assimilation technique is able to modify the simulations to track the
measurements correctly even if the simulations were started with an erroneous
ignition location that is quite far away from the correct one.Comment: 35 pages, 12 figures; minor revision January 2008. Original version
available from http://www-math.cudenver.edu/ccm/report
A dynamic data driven wildland fire model
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, onsite data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth
A wildland fire model with data assimilation
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 is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one