A dynamic data-driven application simulation framework for contaminant transport problems

C. C. Douglas; Y. Efendiev

doi:10.1016/j.camwa.2006.05.003

A dynamic data-driven application simulation framework for contaminant transport problems

C. C. Douglas^*, Y. Efendiev

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper. We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.

Original language	English (US)
Pages (from-to)	1633-1646
Number of pages	14
Journal	Computers and Mathematics with Applications
Volume	51
Issue number	11
DOIs	https://doi.org/10.1016/j.camwa.2006.05.003
State	Published - Jun 2006
Externally published	Yes

Bibliographical note

Funding Information:
This research has been supported in part by the National Science Foundations under grants EIA-0219627, EIA-0218229, and EIA-0218721.

Keywords

Automatic model changing
CFD
DDDAS
Multiscale methods
Remote supercomputing
steering

ASJC Scopus subject areas

Modeling and Simulation
Computational Theory and Mathematics
Computational Mathematics

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10.1016/j.camwa.2006.05.003

Cite this

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title = "A dynamic data-driven application simulation framework for contaminant transport problems",

abstract = "We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper. We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.",

keywords = "Automatic model changing, CFD, DDDAS, Multiscale methods, Remote supercomputing, steering",

author = "Douglas, {C. C.} and Y. Efendiev",

note = "Funding Information: This research has been supported in part by the National Science Foundations under grants EIA-0219627, EIA-0218229, and EIA-0218721.",

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doi = "10.1016/j.camwa.2006.05.003",

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AU - Douglas, C. C.

AU - Efendiev, Y.

N1 - Funding Information: This research has been supported in part by the National Science Foundations under grants EIA-0219627, EIA-0218229, and EIA-0218721.

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N2 - We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper. We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.

AB - We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper. We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.

KW - Automatic model changing

KW - CFD

KW - DDDAS

KW - Multiscale methods

KW - Remote supercomputing

KW - steering

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M3 - Article

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IS - 11

ER -

A dynamic data-driven application simulation framework for contaminant transport problems

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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