A Note on Data-Driven Contaminant Simulation

  • Craig C. Douglas
  • Chad E. Shannon
  • Yalchin Efendiev
  • Richard Ewing
  • Victor Ginting
  • Raytcho Lazarov
  • Martin J. Cole
  • Greg Jones
  • Chris R. Johnson
  • Jennifer Simpson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3038)

Abstract

In this paper we introduce a numerical procedure for performing dynamic data driven simulations (DDDAS). The main ingredient of our simulation is the multiscale interpolation technique that maps the sensor data into the solution space. We test our method on various synthetic examples. In particular we show that frequent updating of the sensor data in the simulations can significantly improve the prediction results and thus important for applications. The frequency of sensor data updating in the simulations is related to streaming capabilities and addressed within DDDAS framework. A further extension of our approach using local inversion is also discussed.

Keywords

Permeability Covariance Upscaling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Craig C. Douglas
    • 1
    • 2
  • Chad E. Shannon
    • 1
  • Yalchin Efendiev
    • 3
  • Richard Ewing
    • 3
  • Victor Ginting
    • 3
  • Raytcho Lazarov
    • 3
  • Martin J. Cole
    • 4
  • Greg Jones
    • 4
  • Chris R. Johnson
    • 4
  • Jennifer Simpson
    • 4
  1. 1.Department of Computer ScienceUniversity of KentuckyLexingtonUSA
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA
  3. 3.ISCTexas A&M UniversityCollege StationUSA
  4. 4.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA

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