Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

Bao, Han; Zhang, Hongbin; Shorthill, Tate; Chen, Edward; Lawrence, Svetlana

doi:10.1016/j.ress.2022.108973

Title: Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

Journal Article · Sun Nov 13 00:00:00 EST 2022 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2022.108973· OSTI ID:1974870

^[1]; Zhang, Hongbin ^[2]; Shorthill, Tate ^[3]; Chen, Edward ^[4]; Lawrence, Svetlana ^[1]

Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Terrapower, Bellevue, WA (United States)
University of Pittsburgh (United States)
North Carolina State University, Raleigh, NC (United States)

Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. Here this paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented here in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517; AC07–05ID14517

OSTI ID:: 1974870

Alternate ID(s):: OSTI ID: 1899216

Report Number(s):: INL/JOU-22-66262-Revision-0; TRN: US2314001

Journal Information:: Reliability Engineering and System Safety, Vol. 230, Issue -; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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