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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.”
Mihaela Ionescu-Bujor, Dan G. Cacuci
Nuclear Science and Engineering | Volume 147 | Number 3 | July 2004 | Pages 189-203
Technical Paper | doi.org/10.13182/NSE03-105CR
Articles are hosted by Taylor and Francis Online.
Sensitivity and uncertainty analysis is becoming increasingly widespread in many fields of engineering and sciences, encompassing practically all of the experimental data-processing activities and many computational modeling and process simulation activities. There are many methods, based either on deterministic or statistical concepts, for performing sensitivity and uncertainty analysis. However, a precise, unified terminology across all methods does not seem to exist, yet often, identical words (e.g., "sensitivity") may not necessarily describe identical quantities, particularly when stemming from conceptually distinct (statistical versus deterministic) methods. Furthermore, the relative strengths and weaknesses of the various methods do not seem to have been reviewed comparatively in the literature published thus far.This paper is the first part of a comparative review, written in two parts, that focuses on the salient features of the statistical and deterministic methods currently used for local and global sensitivity and uncertainty analysis of both large-scale computational models and indirect experimental measurements. Deterministic methods are analyzed in Part I, while statistical methods are highlighted in Part II.Part I of this review commences by highlighting the deterministic methods for computing local sensitivities, namely, the so-called Brute-Force Method (based on recalculations), the Direct Method (including the Decoupled Direct Method), the Green's Function Method, the Forward Sensitivity Analysis Procedure (FSAP), and the Adjoint Sensitivity Analysis Procedure (ASAP). Except for the Brute-Force Method, it is emphasized that local sensitivities can be computed exactly and exhaustively only by using deterministic methods. Furthermore, it is noted that the Direct Method and the FSAP require at least as many model evaluations as there are parameters, while the ASAP requires a single model evaluation of an appropriate adjoint model whose source term is related to the response under investigation. If this adjoint model is developed simultaneously with the original model, then the adjoint model requires relatively modest additional resources to develop and implement. If, however, the adjoint model is constructed a posteriori, considerable skills may be required for its successful development and implementation. Nevertheless, the ASAP is the most efficient method to use for computing local sensitivities of large-scale systems, where the number of parameters, and parameter variations, exceeds the number of responses of interest.The Global ASAP (GASAP) is also highlighted as it appears to be the only deterministic method published thus far for performing genuinely global analysis of nonlinear systems. The GASAP uses both the forward and the adjoint sensitivity systems to explore, exhaustively and efficiently, the entire phase-space of system parameters and dependent variables in order to obtain complete information about the important global features of the physical system, namely, the critical points of the response and the bifurcation branches and/or turning points of the system's state variables.