Kelly, D L | Search NASA

DOE OSTI2011

Heartbeat Model for Component Failure in Simulation of Plant Behavior

As part of the Department of Energy’s “Light Water Reactor Sustainability Program” (LWRSP), tools and methodology for risk-informed characterization of safety margin are being developed for use in supporting decision-making on plant life extension after the first license renewal. Beginning with the traditional discussion of “margin” in terms of a “load” (a physical challenge to system or component function) and a “capacity” (the capability of that system or component to accommodate the challenge), we are developing the capability to characterize realistic probabilistic load and capacity spectra, reflecting both aleatory and epistemic uncertainty in system behavior. This way of thinking about margin comports with work done in the last 10 years. However, current capabilities to model in this way are limited: it is currently possible, but difficult, to validly simulate enough time histories to support quantification in realistic problems, and the treatment of environmental influences on reliability is relatively artificial in many existing applications. The INL is working on a next-generation safety analysis capability (widely referred to as “R7”) that will enable a much better integration of reliability-related and phenomenology-related aspects of margin. In this paper, we show how to implement cumulative damage (“heartbeat”) models for component reliability that lend themselves naturally to being included as part of the phenomenology simulation. Implementation of this modeling approach relies on the way in which the phenomenology simulation implements its dynamic time step management. Within this approach, component failures influence the phenomenology, and the phenomenology influences the component failures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2010

Bayesian Inference for Time Trends in Parameter Values using Weighted Evidence Sets

There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant in time. As a result, Bayesian inference tends to incorporate many years of plant operation, over which there have been significant changes in plant operational and maintenance practices, plant management, etc. These changes can cause significant changes in parameter values over time; however, failure to perform Bayesian inference in the proper time-dependent framework can mask these changes. Failure to question the assumption of constant parameter values, and failure to perform Bayesian inference in the proper time-dependent framework were noted as important issues in NUREG/CR-6813, performed for the U. S. Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards in 2003. That report noted that “in-dustry lacks tools to perform time-trend analysis with Bayesian updating.” This paper describes an applica-tion of time-dependent Bayesian inference methods developed for the European Commission Ageing PSA Network. These methods utilize open-source software, implementing Markov chain Monte Carlo sampling. The paper also illustrates an approach to incorporating multiple sources of data via applicability weighting factors that address differences in key influences, such as vendor, component boundaries, conditions of the operating environment, etc.

99 GENERAL AND MISCELLANEOUS↗