Abstract
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.
Original language | English |
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Article number | 6327213 |
Pages (from-to) | 2088-2094 |
Number of pages | 7 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Volume | 18 |
Issue number | 12 |
DOIs | |
State | Published - 2012 |
Externally published | Yes |
Funding
The authors would like to thank Mark Freshley, Michael Truex (PNNL), Laura Monroe, Paul Weber (LANL), Gregory Flach, Deno Karapatakis (SRNL), Deb Agarwal, Boris A. Faybishenko, John Peterson, and Arie Shoshani (LBNL). This work was supported by the Assistant Secretary for Environmental Management, Office of Environmental Management, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
Keywords
- Visual analytics
- data management
- environmental management
- high-performance computing
- parallel rendering