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.

Military airspace is increasingly crowded with traditional aircraft competing with new loitering munitions and UAVs. Managing the airspace is therefore more challenging, requiring closer coordination among all the stakeholders. In this paper, we describe the motivation and design of a knowledge-based system that attempts to automate aspects of airspace management, including the detection and resolution of airspace conflicts. We then describe a formative evaluation of the system as compared to human performance of the same task, the evaluation setup, results, and analysis