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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.”
Cristina Rea, Robert S. Granetz
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 89-100
Technical Paper | doi.org/10.1080/15361055.2017.1407206
Articles are hosted by Taylor and Francis Online.
Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges from the DIII-D tokamak with the objective of developing a disruption classification algorithm. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters such as the plasma internal inductance and the Greenwald density fraction , coming from both plasma diagnostics and equilibrium reconstructions. The utilization of dimensionless indicators will facilitate a more direct comparison between different tokamak devices.
In order to eventually develop a robust disruption warning algorithm, we leverage Machine Learning techniques, and in particular, we choose the Random Forests algorithm to explore the DIII-D database. We show the results coming from both binary (disrupted/non-disrupted) and multiclass classification problems. In the latter, the time dependency is introduced through the definition of class labels on the basis of the elapsed time before the disruption (i.e., ‘far from a disruption’, ‘within 350 ms of disruption’, etc.). Depending on the formulation of the problem, overall disruption prediction accuracy up to 90% is demonstrated, approaching 97% when identifying a stable and a disruptive phase for disrupted discharges. The performances of the different Random Forest classifiers are discussed in terms of accuracy, by showing the percentages of successfully detected samples, together with the false positive and false negative rates.