Dynamic Contaminant Identification in Water

Craig C. Douglas1,2, J. Clay Harris3, Mohamed Iskandarani4, Chris R. Johnson5, Robert J. Lodder3, Steven G. Parker5, Martin J. Cole5, Richard Ewing6, Yalchin Efendiev6, Raytcho Lazarov6, and Guan Qin6

1 University of Kentucky, Department of Computer Science, 773 Anderson Hall, Lexington, KY 40506-0046, USA

2 Yale University, Department of Computer Science, P.O. Box 208285 New Haven, CT 06520-8285, USA
douglas-craig@cs.yale.edu

3 University of Kentucky, Department of Chemistry, Lexington, KY, 40506 USA
claymay27@gmail.com
rob.lodder@gmail.com

4 University of Miami, Rosenstiel School of Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, FL 33149-1098, USA
mohamed.iskandarani@rsmas.miami.edu

5 University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT 84112, USA
crj@cs.utah.edu
sparker@cs.utah.edu
mjc@cs.utah.edu

6 Texas A&M University, Institute for Scientific Computation, 612 Blocker, 3404 TAMU, College Station, TX 77843-3404, USA
richard_ewing@tamu.edu
efendiev@math.tamu.edu
lazarov@math.tamu.edu
guan.qin@tamu.edu

Abstract. We describe how we plan to convert a traditional data collection sensor and ocean model into a DDDAS enabled system for identifying contaminants and then reacting with different models, simulations, and sensing strategies in a symbiotic manner. The sensor is just as useful in water as it would be on Mars for material identification. A successful terrestrial application of the sensor will lead to many new applications of the device and possible technology transfer to the private sector.

LNCS 3993, pp. 393-400.

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