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Issue No. 12 - Dec. (2012 vol. 18)
ISSN: 1077-2626
pp: 2088-2094
Timothy C. Johnson , Pacific Northwest National Laboratory
Ben Torkian , Savannah River National Laboratory
Glenn Taylor , Savannah River National Laboratory
Phil Moore , Savannah River National Laboratory
Richard Strelitz , Los Alamos National Laboratory
Laura Monroe , Los Alamos National Laboratory
Elizabeth H. Keating , Los Alamos National Laboratory
Alexandru Romosan , Lawrence Berkeley National Laboratory
Harinarayan Krishnan , Lawrence Berkeley National Laboratory
Susan S. Hubbard , Lawrence Berkeley National Laboratory
Jennifer L. Horsman , Lawrence Berkeley National Laboratory
E. Wes Bethel , Lawrence Berkeley National Laboratory
Joerg Meyer , Lawrence Berkeley National Laboratory
Ian Gorton , Pacific Northwest National Laboratory
The U.S. Department of Energy's (DOE) Office of Environmental Management (DOE/EM) currently supports an effort to understand and predict the fate of nuclear contaminants and their transport in natural and engineered systems. Geologists, hydrologists, physicists and computer scientists are working together to create models of existing nuclear waste sites, to simulate their behavior and to extrapolate it into the future. We use visualization as an integral part in each step of this process. In the first step, visualization is used to verify model setup and to estimate critical parameters. High-performance computing simulations of contaminant transport produces massive amounts of data, which is then analyzed using visualization software specifically designed for parallel processing of large amounts of structured and unstructured data. Finally, simulation results are validated by comparing simulation results to measured current and historical field data. We describe in this article how visual analysis is used as an integral part of the decision-making process in the planning of ongoing and future treatment options for the contaminated nuclear waste sites. Lessons learned from visually analyzing our large-scale simulation runs will also have an impact on deciding on treatment measures for other contaminated sites.
Data visualization, Pollution measurement, Data models, Visual analytics, Google, Computational modeling, Monitoring, Environmental management, environmental management, Visual analytics, high-performance computing, data management, parallel rendering
Timothy C. Johnson, Ben Torkian, Glenn Taylor, Phil Moore, Richard Strelitz, Laura Monroe, Elizabeth H. Keating, Alexandru Romosan, Harinarayan Krishnan, Susan S. Hubbard, Jennifer L. Horsman, E. Wes Bethel, Joerg Meyer, Ian Gorton, "Visual Data Analysis as an Integral Part of Environmental Management", IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 2088-2094, Dec. 2012, doi:10.1109/TVCG.2012.278
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