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ANS Student Conference 2025
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Fusion Science and Technology
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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.”
Sergey Y. Medvedev, Alexander A. Martynov, Maxim Y. Isaev, Ivan M. Balachenkov, Nikolai N. Bakharev, Yury V. Petrov, Wilfred A. Cooper
Fusion Science and Technology | Volume 78 | Number 7 | October 2022 | Pages 528-536
Technical Paper | doi.org/10.1080/15361055.2022.2066048
Articles are hosted by Taylor and Francis Online.
This paper presents the results of numerical modeling of the spatial structure and saturation of Alfvén eigenmodes in the GLOBUS-M spherical tokamak with the KINX and VENUS codes. Measurements with the multichannel Doppler backscattering reflectometry provided experimental evidence of the mode localization near the plasma boundary when excited by energetic particles during neutral beam injection heating. The numerical results suggest the Alfvén-sound eigenmode, in particular the beta-induced Alfvén acoustic eigenmode, as the candidate instability responsible for the observed localization pattern. The mode linear growth rates and nonlinear saturation levels are found to be highly sensitive to the parameters of the model.
