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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.”
Dongjune Chang, Maolong Liu, Youho Lee (Univ of New Mexico)
Proceedings | Advances in Thermal Hydraulics 2018 | Orlando, FL, November 11-15, 2018 | Pages 212-226
A Loss of Flow Accident (LOFA) is an accident that causes cooling to slow down due to pump failure or stopping during operation. A fast or slow change in two-phase flow, when overlooked, can lead to an accident like LOFA, and thus, understanding its nature is essential for nuclear reactor safety. In this paper, we demonstrate that using one of the machine learning techniques called Support Vector Machine, one can find the most important factors in two-phase flow change. Using one of the commercial thermal hydraulics analysis code, MARS (A multi-dimensional thermal-hydraulic system code), simulation results were obtained for several scenarios where the mass flow rate decreased sharply. The transient flow change phenomenon near a single PWR rod, which is the simplest case of the reactor, is modeled. The outlet temperature of the coolant which is the final output factor of the transient flow change and the peak temperature of the cladding rod are very important factors for safety analysis. We also show that the outlet temperature profile of the coolant can be used to predict the unknown mass flux and the peak temperature of the cladding rod using the Multi-class Support vector machine algorithm. These results suggest that machine learning techniques may be used to analyze the complex systems of accidents that may occur in the nuclear system.