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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.”
Jeremy A. Roberts, Bradley T. Rearden, Paul P. H. Wilson
Nuclear Science and Engineering | Volume 173 | Number 1 | January 2013 | Pages 43-57
Technical Paper | doi.org/10.13182/NSE10-109
Articles are hosted by Taylor and Francis Online.
This paper presents a method for determining partial biases and bias uncertainties for application in fission product burnup credit criticality safety analysis. The contribution of each nuclide to the overall system keff bias and the bias uncertainty are determined via the generalized linear least squares method. Where experimental benchmarks are available to validate specific nuclides, sensitivity and uncertainty analysis is used to project biases observed in the benchmarks to biases appropriate for the safety system. Two weighting schemes are proposed to produce an overall bias in the safety system from several single partial biases. Finally, these methods are used to determine partial biases for 149Sm and 103Rh from two experiment series and to apply these biases to a representative used fuel safety system. The biases obtained are compared to bounding estimates, and the sensitivity of the results to relevant assumptions is addressed.