Award Abstract #0540153 DDDAS-TMRP: Collaborative Research: Adaptive Data-Driven Sensor Configuration, Modeling, and Deployment for Oil, Chemical, and Biological Contamination near Coastal Facilities
Anita J. LaSalle
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
Start Date:
October 1, 2005
Expires:
September 30, 2007 (Estimated)
Awarded Amount to Date:
$80412
Investigator(s):
Christopher Johnson crj@sci.utah.edu (Principal Investigator)
Steven Parker (Co-Principal Investigator)
Sponsor:
University of Utah
75 S 2000 E
SALT LAKE CITY, UT 84112 801/581-6903
NSF Program(s):
ITR-DYNAMIC DATA DRIV APP SYS
Field Application(s):
0000912 Computer Science
Program Reference Code(s):
HPCC, 9218
Program Element Code(s):
7581
ABSTRACT
The project is aimed at developing a variable light wave sensor array that we will integrate into an ocean observational system. This system will be superior to most near coastal ocean models, which are typically wind driven but not contamination transport driven, in that our new model will be both. These objectives will be accomplished through the dynamic injection of observed ocean data into multiscale mathematical models and computer simulations. The project will create research topics in multiscale mathematics, statistics, and software application integration with a flexible, Grid-based
database and problem solving environment. The project will follow an integrated approach that addresses technical issues at each step of the process: 1) the dynamic simulation instructs the sensors what to look for and reprograms it for those analytes, 2) the sensors report to the simulation the new observed data, and 3) the simulation then incorporates the new data, updates its predictions, and reprograms the sensors as necessary in a closed loop. We will reduce the amount of human intervention needed to monitor spills and other contamination events, making DDDAS viable for sensors going to locations that are difficult to communicate with the sensors in real-time (e.g., an unreliable satellite link or another planetary body in the future). The work will build on the successful results of research previously funded by the NSF, including the SURA Coastal Ocean Observation and Prediction and two ITR projects to develop algorithms, error controls, and
middleware to optimally manage provably scalable computing resources for Grid computing. The project has the ultimate objective to guide the development of hardware and software to enable performing both lab and ocean test, but these tasks will be relegated for follow-on efforts to the present project. Both academic and industrial partners will be involved in the present effort. The research in this project will be extendable to other application environments.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Munzner, T; Johnson, C; Moorhead, R; Pfister, H; Rheingans, P; Yoo, TS. "NIH-NSF Visualization Research Challenges report summary," IEEE COMPUTER GRAPHICS AND APPLICATIONS, v.26, 2006, p. 20-24.
Please report errors in award information by writing to: awardsearch@nsf.gov.
