National Certification Methodology for the Nuclear Weapons Stockpile

Goodwin, Ben; Juzaitis, R J

doi:10.2172/929177

Cited by 10 publications

(13 citation statements)

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“…In the recent years, a number of studies were reported on the theoretical development and the application of the QMU concept in the certification of reliability and safety of nuclear weapons stockpile [1][2][3][4][5]. The description of the key elements of a QMU framework that can be used to address risk and risk mitigation for the certification of nuclear weapons was presented by Sharp and Wood-Schultz [1].…”

Section: Qmu Methodology and Confidence Ratiomentioning

confidence: 99%

Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

Shah

Hosder

Winter

2015

Reliability Engineering & System Safety

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Section: Qmu Methodology and Confidence Ratiomentioning

confidence: 99%

Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

Shah

Hosder

Winter

2015

Reliability Engineering & System Safety

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“…It is interesting to note that the use of absolute bounds is essentially QMU, in its original formulation [4]. F is the metric, the deviations are measured with respect to the design point,…”

Section: Theorem 21 Let B F ( ) Be the Mcdiarmid Bound For Probabilmentioning

confidence: 99%

On the Application of McDiarmid's Inequality to Complex Systems

Wallstrom¹

2017

SIAM/ASA J. Uncertainty Quantification

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Abstract.McDiarmid's inequality has recently been proposed as a tool for setting margin requirements for complex systems. If F is the bounded output of a complex system, depending on a vector of n bounded inputs, this inequality provides a bound BF ( ), such that the probability of a deviation exceeding BF ( ) is less than . I compare this bound with the absolute bound, based on the range of F . I show that when n eff , the effective number of independent variates, is small, and when is small, the absolute bound is smaller than BF ( ), while also providing a smaller probability of exceeding the bound, i.e., zero instead of . Thus, for BF ( ) to be useful, the number of inputs must be large, with a small dependence on any single input, which is consistent with the usual guidance for application of concentration-of-measure results. When the number of inputs is small, or when a small number of inputs account for much of the uncertainty, the absolute bounds will provide better results. The use of absolute bounds is equivalent to the original formulation of the method of quantification of margins and uncertainties (QMU).

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“…Quantification of Margins and Uncertainty (QMU) is a principle means and metric by which nuclear weapons performance is measured [1,2]. At Lawrence Livermore National Laboratory (LLNL), Nuclear weapon QMU is predominately a Physics assessment of the performance margin and associated uncertainties of not achieving the designed nuclear performance.…”

Section: Introductionmentioning

confidence: 99%

Applying QMU to Nuclear Weapon Stewardship

Raboin¹

2013

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The academic bases of QMU are rooted in statistics and engineering reliability or design reliability methods. The following list of textbook references provides instruction on margin over variance ratios and how they relate to the probability of failure [3][4][5][6][7][8][9][10][11]. The earliest mention [12] of a probabilistic failure approach based on overlapping statistical distributions of loadings and strengths come from a publication by Sir Alfred Pugsley in 1939 [13]. He recognized that statistical variations in the strengths of aircraft when subjected to probabilistic variations in aircraft loadings could result in airframe failures.Descriptively, the QMU Confidence Factor (CF) is a metric defined as the performance margin divided by the performance uncertainties. The margin basis is dependent on a selected failure mode. The CF has similarities to other metrics with names, such as Safety Index, Safety Margin and Reliability Index. QMU can be related to other design metrics such as the engineering safety factor, the Sandia K-factor and first and second order reliability methods (see Appendix A).Aviation [14,15], space vehicles [7,16,17], and probabilistic mechanical design employ a variety of statistical methods, safety factors, and reliability/safety indexes to improve and optimize their designs. In particular, air and spacecraft designs use a reliability requirement and confidence limit specification.

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National Certification Methodology for the Nuclear Weapons Stockpile

Cited by 10 publications

References 0 publications

Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

On the Application of McDiarmid's Inequality to Complex Systems

Applying QMU to Nuclear Weapon Stewardship

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