In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e, Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainty (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. This presentation discusses and illustrates the conceptual and computational basis of QMU in analyses that use computational models to predict the behavior of complex systems. Topics considered include (1) the role of aleatory and epistemic uncertainty in QMU, (2)...
The concept of “margin” has a long history in nuclear licensing and in the codification of good engi...
We examine a conceptual framework for accounting for all sources of uncertainty in complex predictio...
In this paper, we present an integrated framework for quantifying epistemic uncertainty in probabili...
This report describes key ideas underlying the application of Quantification of Margins and Uncertai...
This paper is devoted to some recent developments in uncertainty analysis methods of computer codes ...
There will be simplifying assumptions and idealizations in the availability models of complex proces...
International audienceIn nuclear power plants, probabilistic risk assessment insights contribute to ...
International audienceExplores methods for the representation and treatment of uncertainty in risk a...
Part 1: UQ Need: Risk, Policy, and Decision MakingInternational audienceAn approach to the conversio...
This paper presents the conceptual framework that is being used to define quantification of margins ...
This work is devoted to some recent developments in uncertainty analysis of the computer code respon...
In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nucle...
Quantitative risk assessments are an integral part of risk-informed regulation of current and future...
International audienceIn Nuclear Power Plants, Probabilistic Risk Assessment (PRA) insights contribu...
A quantitative risk analysis (QRA) should provide a broad, informative and balanced picture of risk,...
The concept of “margin” has a long history in nuclear licensing and in the codification of good engi...
We examine a conceptual framework for accounting for all sources of uncertainty in complex predictio...
In this paper, we present an integrated framework for quantifying epistemic uncertainty in probabili...
This report describes key ideas underlying the application of Quantification of Margins and Uncertai...
This paper is devoted to some recent developments in uncertainty analysis methods of computer codes ...
There will be simplifying assumptions and idealizations in the availability models of complex proces...
International audienceIn nuclear power plants, probabilistic risk assessment insights contribute to ...
International audienceExplores methods for the representation and treatment of uncertainty in risk a...
Part 1: UQ Need: Risk, Policy, and Decision MakingInternational audienceAn approach to the conversio...
This paper presents the conceptual framework that is being used to define quantification of margins ...
This work is devoted to some recent developments in uncertainty analysis of the computer code respon...
In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nucle...
Quantitative risk assessments are an integral part of risk-informed regulation of current and future...
International audienceIn Nuclear Power Plants, Probabilistic Risk Assessment (PRA) insights contribu...
A quantitative risk analysis (QRA) should provide a broad, informative and balanced picture of risk,...
The concept of “margin” has a long history in nuclear licensing and in the codification of good engi...
We examine a conceptual framework for accounting for all sources of uncertainty in complex predictio...
In this paper, we present an integrated framework for quantifying epistemic uncertainty in probabili...