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Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
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.