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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.”
Anthony Michael Scopatz
Nuclear Technology | Volume 195 | Number 3 | September 2016 | Pages 273-287
Technical Paper | doi.org/10.13182/NT15-153
Articles are hosted by Taylor and Francis Online.
This paper presents a new fuel cycle benchmarking analysis methodology by coupling Gaussian process (GP) regression, a popular technique in machine learning, to dynamic time warping, a mechanism widely used in speech recognition. Together, they generate figures of merit (FOMs) for a suite of fuel cycle realizations. The FOMs may be computed for any time series metric that is of interest to a benchmark. For a given metric, these FOMs have the advantage that they reduce the dimensionality to a scalar and are thus directly comparable. The FOMs account for uncertainty in the metric itself, utilize information across the whole time domain, and do not require that the simulators use a common time grid. Here, a distance measure is defined that can be used to compare the performance of each simulator for a given metric. Additionally, a contribution measure is derived from the distance measure that can be used to rank order the impact of different partitions of a fuel cycle metric. Lastly, this paper warns against using standard signal-processing techniques for error reduction, as error reduction is better handled by the GP regression itself.