Handbook of Parameter Estimation for Probabilistic Risk Assessment (NUREG/CR-6823, SAND2003-3348P)

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• Publication Information
• Abstract

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Publication Information

Manuscript Completed: November 2002
Date Published: September 2003

Prepared by:
Corwin L. Atwood¹, Jeffrey L. LaChance², Harry F. Martz³, Dennis J. Anderson²,
Max Englehardt⁴, Donnie Whitehead², Tim Wheeler²

¹Statwood Consulting
Silver Spring, Maryland 20910

²Sandia National Laboratories
P.O. Box 5800
Albuquerque, New Mexico 87185-0748

³Los Alamos National Laboratory
Los Alamos, New Mexico 87545

⁴Formerly with Idaho National Engineering and Environmental Laboratory
Idaho Falls, Idaho 83415

Arthur D. Salomon, NRC Technical Monitor

Prepared for:
Division of Risk Analysis and Applications
Office of Nuclear Regulatory Research
U.S. Nuclear Regulatory Commission
Washington, DC 20555-0001

NRC Job Code W6970

Availability Notice

Abstract

Probabilistic risk assessment (PRA) is a mature technology that can provide a quantitative assessment of the risk from accidents in nuclear power plants. It involves the development of models that delineate the response of systems and operators to accident-initiating events. Additional models are generated to identify the component failure modes required to cause the accident-mitigating systems to fail. Each component failure mode is represented as an individual "basic event" in the systems models. Estimates of risk are obtained by propagating the uncertainty distributions for each of the parameters through the PRA models.

The data analysis portion of a nuclear power plant PRA provides estimates of the parameters used to determine the frequencies and probabilities of the various events modeled in a PRA. This handbook provides guidance on sources of information and methods for estimating the parameters used in PRA models and for quantifying the uncertainties in the estimates. This includes determination of both plant-specific and generic estimates for initiating event frequencies, component failure rates and unavailabilities, and equipment non-recovery probabilities.