Dynamic Data-Driven Contaminant Simulation

Craig C. Douglas (), Yalchin Efendiev (), Richard Ewing (), Victor Ginting, Raytcho Lazarov (), Martin J. Cole (), Greg Jones () and Chris R. Johnson ()
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Craig C. Douglas: University of Kentucky, Department of Computer Science
Yalchin Efendiev: Texas A&M University
Richard Ewing: Texas A&M University
Victor Ginting: Texas A&M University
Raytcho Lazarov: Texas A&M University
Martin J. Cole: University of Utah, Scientific Computing and Imaging Institute
Greg Jones: University of Utah, Scientific Computing and Imaging Institute
Chris R. Johnson: University of Utah, Scientific Computing and Imaging Institute

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 25-36 from Springer

Abstract: Summary In this paper we discuss a numerical procedure for performing dynamic data driven simulations (DDDAS). In dynamic data driven simulations our goal is to update the solution as well as input parameters involved in the simulation based on local measurements. The updates are performed in time. In the paper we discuss (1) updating the solution using multiscale interpolation technique (2) recovering as well as updating initial conditions based on least squares approach (3) updating the permeability field using Markov Chain Monte Carlo techniques. We test our method on various synthetic examples.

Keywords: Markov Chain Monte Carlo; Sensor Data; Time Level; Sensor Location; Soft Constraint (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_3

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DOI: 10.1007/3-540-27912-1_3

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