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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.”
Stephen D. Unwin, Peter P. Lowry, Michael Y. Toyooka
Nuclear Science and Engineering | Volume 171 | Number 1 | May 2012 | Pages 69-77
Technical Paper | doi.org/10.13182/NSE11-18
Articles are hosted by Taylor and Francis Online.
Conventional probabilistic risk assessments (PRAs) are not well suited to addressing long-term reactor operations. Since passive structures and components are among those for which replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant; yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA presents challenges. Service data to quantify component reliability models are sparse, and this is exacerbated by the greater data demands of age-dependent reliability models. Another factor is that there can be numerous potential degradation mechanisms associated with the materials and operating environment of a given component. This deepens the data problem since risk-informed management of component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates metrics of materials degradation susceptibility with available plant service data to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by predicted long-term operational risk.