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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.”
L. A. Belblidia, J. M. Kallfelz, D. G. Cacuci
Nuclear Science and Engineering | Volume 84 | Number 3 | July 1983 | Pages 206-225
Technical Paper | doi.org/10.13182/NSE83-A17790
Articles are hosted by Taylor and Francis Online.
This paper presents an efficient method to analyze variations that nuclear data perturbations induce in one-dimensional power-density distributions. This method is called the Taylor-generalized perturbation theory (Taylor-GPT) method since it is based on (a) use of a Taylor series expansion of the response variation, and (b) use of generalized perturbation theory (GPT) to evaluate the derivative operators that appear as coefficients in this Taylor series. Equations satisfied by the importance functions for the derivatives of the response variations are derived and solved with existing GPT codes. The characteristics of these functions are highlighted analytically. Particular attention is focused on the numerical value and location of the maximum power density. This is because perturbations in system parameters affect not only the value at the maximum, but also the location of this maximum. The Taylor-GPT method can efficiently assess such effects. The practical usefulness of the Taylor-GPT method is illustrated by considering test cases involving a simplified heterogeneous liquid-metal fast breeder reactor model. The results indicate that this method is as accurate as the GPT method, yet requires fewer calculations when investigating space-dependent power density variations.