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ANS Student Conference 2025
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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 Science and Engineering | Volume 186 | Number 1 | April 2017 | Pages 83-97
Technical Paper | doi.org/10.1080/00295639.2016.1272384
Articles are hosted by Taylor and Francis Online.
A method for quickly determining deployment schedules that meet any given fuel cycle demands is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of deployed facilities. Each of these predictions is measured against the demand curve using the dynamic time warping distance. The minimum-distance deployment schedule is evaluated in a full fuel cycle simulation, and the generated production curve then informs the model on the next optimization iteration. The method converges within five to ten iterations to a distance that is less than 1% of the total deployable production. This speed of convergence makes it suitable for use even when fuel cycle realizations are expensive, as in higher-fidelity or agent-based simulators. A representative once-through fuel cycle is used to demonstrate the methodology for reactor deployment. However, the algorithm itself is multivariate and may be used to determine the deployment schedules of many facility types that meet a number of independent criteria simultaneously. The once-through, electricity production example was chosen for the simplicity of illustrating the method.