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Dynamic Data-Driven Contaminant Simulation

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Craig C. Douglas

    (University of Kentucky, Department of Computer Science
    Yale University, 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)

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.

Suggested Citation

  • Craig C. Douglas & Yalchin Efendiev & Richard Ewing & Victor Ginting & Raytcho Lazarov & Martin J. Cole & Greg Jones & Chris R. Johnson, 2005. "Dynamic Data-Driven Contaminant Simulation," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 25-36, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_3
    DOI: 10.1007/3-540-27912-1_3
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