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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.”
Zhichao Guo, Robert E. Uhrig
Nuclear Technology | Volume 99 | Number 1 | July 1992 | Pages 36-42
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT92-A34701
Articles are hosted by Taylor and Francis Online.
A hybrid artificial neural network is used to model the thermodynamic behavior of the Tennessee Valley Authority’s Sequoyah nuclear power plant using data for heat rate measurements acquired over a 1-yr period. The modeling process involves the use of a selforganizing network to rearrange the original data into several classes by clustering. Then, the centroids of these clusters are used as the training patterns for an artificial neural network that utilizes backpropagation training to adjust the weights on the connections between artificial neurons. This procedure greatly reduces the training time and reduces the system error. Comparison of the calculated heat rates with those predicted by the artificial neural network gives an error of <0.1%. A sensitivity analysis is then performed by taking the partial derivative of the heat rate with respect to each individual input to secure a sensitivity coefficient. These coefficients identified the input variables that were most important to improving the heat rate and efficiency. The methodology reported is an alternative to the conventional modeling procedures used in other heat rate monitoring systems. It has the advantage that the artificial neural network model is based on actual plant data that cover the dynamic range normally occurring over an annual cycle of operation, and it is not subject to linearization or empirical approximations. This process could be utilized by existing heat rate monitoring systems.