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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.”
Sumeet Chhibber, George E. Apostolakis, David Okrent
Nuclear Technology | Volume 105 | Number 1 | January 1994 | Pages 87-103
Technical Paper | Special on Nuclear Criticality Safety / Nuclear Reactor Safety | doi.org/10.13182/NT94-A34913
Articles are hosted by Taylor and Francis Online.
The use of expert judgments in probabilistic risk assessments has become common. Simple aggregation methods have often been used with the result that expert biases and interexpert dependence are often neglected. Sophisticated theoretical models for the use of expert opinions have been proposed that offer ways of incorporating expert biases and dependence, but they have not found wide acceptance because of the difficulty and rigor of these methods. Practical guidance on the use of the versatile Bayesian expert judgment aggregation model is provided. In particular, the case study of pressure increment due to vessel breach in the Sequoyah nuclear power plant is chosen to illustrate how phenomenological uncertainty can be addressed by using the Bayesian aggregation model. The results indicate that the Bayesian aggregation model is a suitable candidate model for aggregating expert judgments, especially if there is phenomenological uncertainty. Phenomenological uncertainty can be represented through the dependence parameter of the Bayesian model. This is because the sharing of assumptions by the experts tends to introduce dependence between the experts. The extent of commonality in the experts’ beliefs can be characterized by assessing their interdependence. The results indicate that uncertainty is possibly underestimated by ignoring dependence. Two Bayesian approaches are used. The first approach uses the experts’ opinions as evidence to update the decision maker’s state of knowledge. The second approach, in recognition of the fact that the experts are highly dependent on a common information source, assumes that the common information source is the actual expert and the participants are assessing its biases and credibility. The results lend validity to the use of weighted averaging schemes because the Bayesian aggregation method encompasses simple arithmetic and geometric averaging techniques.