Development of Quantitative Software Reliability Models for Digital Protection Systems of Nuclear Power Plants (NUREG/CR-7044)

This NUREG publication has been issued for public comment. The comment period is now closed.

On this page:

• Publication Information
• Abstract

Download complete document

• NUREG/CR-7044 (PDF - 8.79 MB)

Publication Information

Manuscript Completed: June 2011
Date Published: July 2011

Prepared by:
Tsong-Lun Chu
Meng Yue
Gerardo Martinez-Guridi
John Lehner

Brookhaven National Laboratory
P.O. Box 5000
Upton, NY 11973

Alan Kuritzky, NRC Project Manager

NRC Job Code N6919

Office of Nuclear Regulatory Research
U.S. Nuclear Regulatory Commission
Washington DC 20555-0001

Availability Notice

Abstract

The U.S. Nuclear Regulatory Commission is currently performing research on the development of probabilistic models for digital instrumentation and control systems for inclusion in nuclear power plant (NPP) probabilistic risk assessments. As part of this research, Brookhaven National Laboratory (BNL) is exploring the inclusion of software failures into digital system reliability models. A previous BNL technical report, entitled "Review of Quantitative Software Reliability Methods," BNL-94047-2010 (ADAMS Accession No. ML102240566), documented a review of currently available quantitative software reliability methods (QSRMs) that can be used to quantify software failure rates and probabilities of digital systems at NPPs and identified a set of desirable characteristics for QSRMs. In the current report, two candidate QSRMs are selected based on a structured comparison of the previously-identified QSRMs against the set of desirable characteristics. Each selected method is further developed in preparation to be applied in a case study. This report also identifies an example digital protection system for use in the case studies. The actual case studies will be documented in separate reports. Completion of the case studies is expected to provide a much better understanding of the existing capabilities and limitations in treating software failure in digital system reliability models.