Contribution to Nuclear Safety Demonstration Through System Modelling and Artificial Intelligence. Issue 4 (12th January 2022)

• Record Type: Journal Article
• Authors: Roumili, Emir; Bossu, Jean‐François; Chapurlat, Vincent; Daclin, Nicolas; Tixier, Jérôme; Plana, Robert
• Is Part Of: Insight. Volume 24:Issue 4(2021)
• Page Start: 31
• Page End: 33
• Publication Date: 2022-01-12
• Subjects: Nuclear Safety -- Systems Engineering -- Model‐Based System Engineering -- Requirements Engineering -- Machine Learning -- Natural Language Processing -- NLP -- Licensing; Systems engineering -- Periodicals; Systems engineering; Periodicals; 620.0011
• DOI: 10.1002/inst.12360
• Languages: English
• ISSNs: 2156-485X

Abstract

ABSTRACT: Nuclear Power Plant (NPP) engineering projects become increasingly complex. For instance, a nuclear reactor includes more than 50 buildings, 500 km of piping, 500, 000 components and 100 million units of data (requirements, reports, schemes …). However, nuclear safety demonstration of any nuclear facility is at the heart of the nuclear industry, being the most important and limiting factor for all requested engineering activities. Ensuring all these activities are performed considering safety demonstration is mandatory to get permission to license, build, operate, dismantle, and more. This article synthetizes an innovative method that mixes and takes advantages of artificial intelligence techniques and systems engineering principles, processes, and model‐based systems engineering (MBSE) principles and usages. This method aims to guide and support engineers to improve their vision, their knowledge and vocabulary, and their capacities in terms of, first, safety requirements elicitation, and second of safety requirements demonstration.