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The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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General Kenneth Nichols and the Manhattan Project
Nichols
The Oak Ridger has published the latest in a series of articles about General Kenneth D. Nichols, the Manhattan Project, and the 1954 Atomic Energy Act. The series has been produced by Nichols’ grandniece Barbara Rogers Scollin and Oak Ridge (Tenn.) city historian David Ray Smith. Gen. Nichols (1907–2000) was the district engineer for the Manhattan Engineer District during the Manhattan Project.
As Smith and Scollin explain, Nichols “had supervision of the research and development connected with, and the design, construction, and operation of, all plants required to produce plutonium-239 and uranium-235, including the construction of the towns of Oak Ridge, Tennessee, and Richland, Washington. The responsibility of his position was massive as he oversaw a workforce of both military and civilian personnel of approximately 125,000; his Oak Ridge office became the center of the wartime atomic energy’s activities.”
Robert W. Rice, John C. Walton
Nuclear Technology | Volume 163 | Number 1 | July 2008 | Pages 15-23
Technical Paper | High-Level Radioactive Waste Management | doi.org/10.13182/NT08-A3965
Articles are hosted by Taylor and Francis Online.
A numerical experiment was performed in order to examine the ability of multiple Monte Carlo realizations of a numerical model to reproduce the risk from a hypothetically known waste disposal situation. In the analysis, the risk was summarized by several risk metrics that could be chosen by a regulatory agency to set a risk standard. In the numerical experiment, the parameters in the numerical model are systematically varied to adjust bias (conservative or nonconservative) and to increase uncertainty relative to the hypothetically known future. The influence of parameter bias and uncertainty on the accuracy of each risk metric in predicting the nominal risk was evaluated and presented graphically. These analyses concluded that the peak-of-the-mean metric provides the least stable and least accurate risk predictions, whereas the cumulative release metric and mean of the peaks are more stable and accurate. The peak-of-the-mean and peak-of-the-median metrics exhibit risk dilution (i.e., a decrease in the predicted risk with increased uncertainty) and tend to underpredict risk. Additionally, these results illustrated how risk predictions that are made using what may be considered "conservative" assumptions can be moved in a direction that may or may not be expected or intended. Simulation relative to a hypothetical future (i.e., the nominal case) provides insight into the numerical behavior and potential accuracy of our risk assessment tools and potential issues with setting regulatory standards.