Using Condition Monitoring Data for Severe Accidents Management

Roma, Giovanni; Di Maio, Francesco; Zio, Enrico

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https://hdl.handle.net/2142/121847 Copy

Description

Title

Using Condition Monitoring Data for Severe Accidents Management

Author(s)

• Roma, Giovanni
• Di Maio, Francesco
• Zio, Enrico

Issue Date

2023

Keyword(s)

• Nuclear systems
• Severe accident management guidelines (SAMGs)
• Dynamic Bayesian network
• VVER-1000
• Loss of coolant accident (LOCA)

Abstract

Severe Accident Management Guidelines (SAMGs) consist in a series of prescriptive actions to mitigate the consequences of Nuclear Power Plant (NPP) accidents, and their design is based on expert-postulated prototypical severe accident scenarios. In this paper, we propose to augment the capabilities of SAMGs by informing them with real-time condition monitoring data of the actually developing scenario. The aim is to diagnose the NPP Plant Damage State (PDS) and predict the development of the scenarios. To do this, we feed the condition monitoring data to a Dynamic Bayesian Network (DBN). The proposed method is exemplified on the case study of a Loss of Coolant Accident in a VVER-1000 containment building

Type of Resource

text

Language

eng

Handle URL

https://hdl.handle.net/2142/121847

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