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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.”
Ryan G. McClarren, James Paul Holloway, Thomas A. Brunner, Thomas A. Mehlhorn
Nuclear Science and Engineering | Volume 155 | Number 2 | February 2007 | Pages 290-299
Technical Paper | Mathematics and Computation, Supercomputing, Reactor Physics and Nuclear and Biological Applications | doi.org/10.13182/NSE07-A2663
Articles are hosted by Taylor and Francis Online.
An implicit Riemann solver for the one- and two-dimensional time-dependent spherical harmonics approximation (Pn) to the linear transport equation is presented. This spatial discretization scheme is based on cell-averaged quantities and uses a monotonicity-preserving high resolution method to achieve second-order accuracy (away from extreme points in the solution). Such a spatial scheme requires a nonlinear method of reconstructing the slope within a spatial cell. We have devised a means of creating an implicit (in time) method without the necessity of a nonlinear solver. This is done by computing a time step using a first-order scheme and then, based on that solution, reconstructing the slope in each cell, an implementation that we justify by analyzing the model equation for the method. This quasilinear approach produces smaller errors in less time than both a first-order scheme and a method that solves the full nonlinear system using a Newton-Krylov method.