Abstract
Nuclear power plants (NPPs) are increasingly being equipped with digital I&C systems. Although some probabilistic safety assessment (PSA) models for the digital I&C of NPPs have been constructed, there is currently no specific internationally agreed guidance for their modeling. Thus, the OECD Nuclear Energy Agency (NEA) Committee on the Safety of Nuclear Installations (CSNI) Working Group on Risk Assessment (WGRISK) initiated a task called “Digital I&C PSA – Comparative application of DIGital I&C Modeling Approaches for PSA” (DIGMAP) in 2017. The PSA models of an exemplary NPP, simplified to focus on digital I&C, developed by six organizations were compared and sensitivity studies on the models were conducted in order to find valuable insights on modeling of digital I&C. The activity resulted in a set of qualitative and quantitative learnings concerning e.g., failure behavior of digital I&C systems, different levels of modeling abstraction and the main elements determining the core damage frequency of the reference case as well as those with only minor effect on the results. The study also highlighted the challenges with modeling of large common cause component groups and the difficulties associated with estimation of key parameters related to software (incl. software probability of failure on demand and common cause failure factors) and coverage of diagnostic tests.
Original language | English |
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Number of pages | 10 |
Publication status | Published - 17 Jun 2021 |
MoE publication type | Not Eligible |
Event | 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC&HMIT 2021: Online - Virtual Duration: 14 Jun 2021 → 17 Jun 2021 Conference number: 12 https://www.ans.org/meetings/npichmit2021/ |
Conference
Conference | 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC&HMIT 2021 |
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Abbreviated title | NPIC&HMIT 2021 |
Period | 14/06/21 → 17/06/21 |
Internet address |
Keywords
- digital I&C
- probabilistic safety assessment
- nuclear power plant
- benchmark
- modeling