International case study comparing PSA modeling approaches for nuclear digital I&C – OECD/NEA task DIGMAP

Markus Porthin (Corresponding Author), Sung Min Shin, Richard Quatrain, Tero Tyrväinen, Jiri Sedlak, Hans Brinkman, Christian Müller, Paolo Picca, Milan Jaros, Venkat Natarajan, Ewgenij Piljugin, Jeanne Demgné

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Nuclear power plants are increasingly being equipped with digital I&C systems. Although some probabilistic safety assessment (PSA) models for the digital I&C of nuclear power plants have been constructed, there is currently no specific internationally agreed guidance for their modeling. This paper presents an initiative by the OECD Nuclear Energy Agency called “Digital I&C PSA – Comparative application of DIGital I&C Modelling Approaches for PSA (DIGMAP)”, which aimed to advance the field towards practical and defendable modeling principles. The task, carried out in 2017–2021, used a simplified description of a plant focusing on the digital I&C systems important to safety, for which the participating organizations independently developed their own PSA models. Through comparison of the PSA models, sensitivity analyses as well as observations throughout the whole activity, both qualitative and quantitative lessons were learned. These include insights on failure behavior of digital I&C systems, experience from models with different levels of abstraction, benefits from benchmarking as well as major contributors to the core damage frequency and those with minor effect. The study also highlighted the challenges with modeling of large common cause component groups and the difficulties associated with estimation of key software and common cause failure parameters.

Original languageEnglish
Pages (from-to)4367-4381
Number of pages15
JournalNuclear Engineering and Technology
Volume55
Issue number12
DOIs
Publication statusPublished - Dec 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Benchmarking
  • Case study
  • Digital I&C
  • Nuclear power plant
  • Probabilistic safety assessment
  • Reliability

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