Abstract: TBD

Abstract: This paper extends FARSITE (a software used for wildfire modeling and simulation) to incorporate data assimilation techniques based on noisy and limited spatial resolution observations of the fire perimeter to improve the accuracy of wildfire spread predictions. To include data assimilation in FARSITE, uncertainty on both the simulated wildfire perimeter and the measured wildfire perimeter is used to formulate optimal updates for the prediction of the spread of the wildfire. For data assimilation, Wildfire perimeter measurements with limited spatial resolution and a known uncertainty are used to to formulate an optimal adjustment in the fire perimeter prediction. The adjustment is calculated from the Kalman filter gain in an Ensemble Kalman filter that exploit the uncertainty information on both the simulated wildfire perimeter and the measured wildfire perimeter. The approach is illustated on a wildfire simulation representing the 2014 Cocos fire and presents comparison results for hourly data assimilation results.

Abstract: The accurate prediction of forest fire propagation is a crucial issue to minimize its effects. So, several models have been developed to determine the forest fire propagation beforehand. Such models require several input parameters that in some cases cannot be known precisely in a real emergency. So, a two stage methodology was developed to calibrate the input parameters to improve the quality of the prediction. This methodology was based on Genetic Algorithms which require the execution of many simulations. Moreover, when the fire front is large some input parameters are not uniform along the whole front and complementary models must be introduced to determine the values of those parameters along the whole area involved. One of these non-uniform parameters is wind. So, in this work a wind field model is introduced. This model implies more computation time and response time is the main constraint. The prediction must be provided as fast as possible to be useful, so it is necessary to exploit all available computing resources. So a Hybrid MPI-OpenMP application has been developed to reach a response in the shortest possible time. This work is focused on reduce the execution time of a worker in a MPI Master/Worker structure analyzing the simulation software parts which compose the Fire Simulator System for large scale fores fires that runs on each worker.

Abstract: Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.

Abstract: This paper shows the use of optimal filter estimation for real-time data processing to automatically detect dynamic transient effects in phasor data produced a synchrophasor vector processing systems. The optimal filters are estimated on the basis of phasor data where no disturbances are present and the estimation problem is formulated as a least squares optimization. Event detection bounds are computed from variance estimates and events are detected by formulating conditions on the number of consecutive samples that filtered phasor signals are outside of the bounds. Event detection is illustrated on the phasor data obtained from a microPMU system developed by Power Standards Lab.

Abstract: This paper presents the first results to combine two theoretically sound methods (spectral projection and multigrid methods) together to attack ill-conditioned linear systems. Our preliminary results show that the proposed algorithm applied to a Krylov subspace method takes much fewer iterations for solving an ill-conditioned problem downloaded from a popular online sparse matrix collection.

Abstract: We discuss decoupling issues in multi-physics and complex system computation. A general framework is presented for decoupling coupled PDE models in multi-physics applications. Examples of decoupled numerical algorithms and theory are illustrated for two-grid/multi-grid methods, preconditioning methods, mixed implicit/explicit marching methods for coupled fluid/porous media flows, fluid-solid interaction, superconductivity, etc

Abstract: Due the recent increase of the volume of data that has been generated, organizing this data has become one of the biggest problems in Computer Science. Among the different strategies propose to deal efficiently and effectively for this purpose, we highlight those related to clustering, more specifically, density-based clustering strategies, which stands out for its ability to define clusters of arbitrary shape and the robustness to deal with the presence of data noise, such as DBSCAN and OPTICS. However, these algorithms are still a computational challenge since they are distance-based proposals. In this work we present a new approach to make OPTICS feasible based on data indexing strategy. Although the simplicity with which the data are indexed, using graphs, it allows explore various parallelization opportunities, which were explored using graphic processing unit (GPU). Based on this structure, the complexity of OPTICS is reduced to O(E*logV) in the worst case, becoming itself very fast. In our evaluation we show that our proposal can be over 200x faster than its sequential version using CPU.