Award Abstract #0540155
DDDAS-TMRP: Collaborative Research: Adaptive Data-Driven Sensor Configuration, Modeling, and Deployment for Oil, Chemical, and Biological Contamination near Coastal Facilities

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 19, 2005
Latest Amendment Date:	September 19, 2005
Award Number:	0540155
Award Instrument:	Standard Grant
Program Manager:	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:	$30001
Investigator(s):	Mohamed Iskandarani miskandarani@rsmas.miami.edu (Principal Investigator)
Sponsor:	University of Miami Rosenstiel School of Marine&Atmospheric Sci 4600 RICKENBACKER CSWY KEY BISCAYNE, FL 33149 305/421-4089
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

C. C. Douglas and Deng Li. "Dynamic Data-Driven Application Simulation Projects," Bulletin of the Japanese Society of Industrial and Applied Mathematics, v.15, 2005, p. 75.

CC Douglas, MJ Cole, P Dostert, Y Efendiev, RE Ewing, G Haas, M Iskandarani, CR Johnson,RA Lodder. "Dynamically identifying and tracking contaminants in water bodies," COMPUTATIONAL SCIENCE - ICCS 2007, v.4487, 2007, p. 1002-1009.

R. Sinha, R.E. Ewing, and R.D. Lazarov. "Some new error estimates of a semidiscrete finite volume element method for parabolic integro-differential equation with nonsmooth initial data,," SIAM Journal on Numerical Analysis, v.43, 2006, p. 2320.

T. Munzner, C.R. Johnson, R. Moorhead, H. Pfister, P. Rheingans, and T. Yoo. "NIH/NSF visualization research challenges report summary," IEEE Computer Graphics and Applications, v.2, 2006, p. 20.

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