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Award Abstract #0218229
Collaborative Research: ITR/AP- Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulation (DDDAS) Techniques
| NSF Org: |
CNS
Division of Computer and Network Systems
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| Initial Amendment Date: |
September 10, 2002 |
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| Latest Amendment Date: |
September 10, 2002 |
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| Award Number: |
0218229 |
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| Award Instrument: |
Standard Grant |
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| Program Manager: |
Frederica Darema
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
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| Start Date: |
September 15, 2002 |
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| Expires: |
August 31, 2006 (Estimated) |
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| Awarded Amount to Date: |
$159990 |
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| Investigator(s): |
Richard Ewing richard-ewing@tamu.edu (Principal Investigator)
Raytcho Lazarov (Co-Principal Investigator)
Yalchin Efendiev (Co-Principal Investigator)
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| Sponsor: |
Texas A&M Research Foundation
400 Harvey Mitchell Parkway, S
College Station, TX 77845 979/845-8600
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| NSF Program(s): |
ITR SMALL GRANTS
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| Field Application(s): |
0000099 Other Applications NEC
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| Program Reference Code(s): |
HPCC, 9218, 1686, 1667
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| Program Element Code(s): |
1686
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ABSTRACT
EIA-0219627 Craig Douglas University of Kentucky Collaborative Research: ITR/AP-Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulation \(DDDAS\) Techniques
This project will lead to a leap-ahead technology in simulation capabilities. Research in the development of new methods and algorithms for the specific application areas is needed. The dynamic application requirements will dictate computing systems' support that includes systems' software technologies, such as active middleware services for real time, dynamic reconfiguration capabilities, resource discovery, load balancing, security, fault tolerance, quality of service, and dynamic interfaces with field measurement systems.
An encoded web stream set of contaminations from actual situations (both above ground and underground) will allow researchers besides us to tap into our virtual reality DDDAS environment. Visualization systems will allow us to work with a variety of real networks, sensors, and environments.
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