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National Nuclear Security Administration, Office of Defense ProgramsBiblio.com

Quantification of Margins and Uncertainties; QMU Workshop August 9-10 2006 Washington DC

2006Best.-Nr. 80402Washington DC: National Nuclear Security Administration Office of Defense Programs 2006. Presumed First thus. Three Ring Binder. Good. This binder has seven tabs five with information. After a table of contents the tabs are: Agenda Attendee Listing NA-11 Opening Comments Draft QMU Business Operating Procedure missing Definition of Terms QMU Site Differences missing and White Paper on "National Certification Methodology for the Nuclear Weapon Stockpile" . The White Paper is 8 pages with color illustrations. The Table of Contents has the name Deeny sic Deeney written in the upper right corner. This is believe to have been the copy of a Dr. Christopher Deeny then a senior executive in the office of Assistant Deputy Administrator for Research Development and Simulation NA-11. He later rose to the Assistant Deputy Administrator position. The agenda included assessments of QMU Methodologies at each lab a W76 Nuclear example a W80 example QMU differences between sites QMU Inconsistencies and a Path Forward discussion. There are 30 attendees listed from Sandia Los Alamos Livermore Argonne DOE Office of Science and NNSA. The NA-11 Opening Comments is a nine vugraph presentation that addressed workshop purpose goals description history external interest and desired outcome. The White Paper was authored by Bruce T. Goodwin then of Livermore and Raymond Juzaitis then of Los Alamos later head of the LLC that operated the Nevada National Security Site. This is a rare surviving snapshot of the U.S. Weapons Program development and assessment of Quantification of Margins and Uncertainties in the context of the U.S. Nuclear Weapons Program absent underground testing. Derived from Wikipeida: Quantification of Margins and Uncertainty QMU is a decision-support methodology for complex technical decisions. QMU focuses on the identification characterization and analysis of performance thresholds and their associated margins for engineering systems that are evaluated under conditions of uncertainty particularly when portions of those results are generated using computational modeling and simulation. QMU has traditionally been applied to complex systems where comprehensive experimental test data is not readily available and cannot be easily generated for either end-to-end system execution or for specific subsystems of interest. Examples of systems where QMU has been applied include nuclear weapons performance qualification and stockpile assessment. QMU focuses on characterizing in detail the various sources of uncertainty that exist in a model thus allowing the uncertainty in the system response output variables to be well quantified. These sources are frequently described in terms of probability distributions to account for the stochastic nature of complex engineering systems. The characterization of uncertainty supports comparisons of design margins for key system performance metrics to the uncertainty associated with their calculation by the model. QMU supports risk-informed decision-making processes where computational simulation results provide one of several inputs to the decision-making authority. There is currently no standardized methodology across the simulation community for conducting QMU; the term is applied to a variety of different modeling and simulation techniques that focus on rigorously quantifying model uncertainty in order to support comparison to design margins. The fundamental concepts of QMU were originally developed concurrently at several national laboratories supporting nuclear weapons programs in the late 1990s including Lawrence Livermore National Laboratory Sandia National Laboratories and Los Alamos National Laboratory. The original focus of the methodology was to support nuclear stockpile decision-making an area where full experimental test data could no longer be generated for validation due to bans on nuclear weapons testing. The methodology has since been applied in other applications where safety or mission critical decisions for complex projects must be made using results based on modeling and simulation. Examples outside of the nuclear weapons field include applications at NASA for interplanetary spacecraft and rover development missile six-degree-of-freedom simulation results and characterization of material properties in terminal ballistic encounters. QMU focuses on quantification of the ratio of design margin to model output uncertainty. The process begins with the identification of the key performance thresholds for the system which can frequently be found in the systems requirements documents. These thresholds also referred to as performance gates can specify an upper bound of performance a lower bound of performance or both in the case where the metric must remain within the specified range. For each of these performance thresholds the associated performance margin must be identified. The margin represents the targeted range the system is being designed to operate in to safely avoid the upper and lower performance bounds. These margins account for aspects such as the design safety factor the system is being developed to as well as the confidence level in that safety factor. QMU focuses on determining the quantified uncertainty of the simulation results as they relate to the performance threshold margins. This total uncertainty includes all forms of uncertainty related to the computational model as well as the uncertainty in the threshold and margin values. The identification and characterization of these values allows the ratios of margin-to-uncertainty to be calculated for the system. These M/U values can serve as quantified inputs that can help authorities make risk-informed decisions regarding how to interpret and act upon results based on simulations. Verification and validation V & V of a model is closely interrelated with QMU. Verification is broadly acknowledged as the process of determining if a model was built correctly; validation activities focus on determining if the correct model was built. V&V against available experimental test data is an important aspect of accurately characterizing the overall uncertainty of the system response variables. V&V seeks to make maximum use of component and subsystem-level experimental test data to accurately characterize model input parameters and the physics-based models associated with particular sub-elements of the system. The use of QMU in the simulation process helps to ensure that the stochastic nature of the input variables as well as the underlying uncertainty in the model are properly accounted for when determining the simulation runs required to establish model credibility prior to accreditation. National Nuclear Security Administration, Office of Defense Programs unknown

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