University of Illinois Urbana-Champaign

Using Condition Monitoring Data for Severe Accidents Management

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

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A06.pdf
P06.pdf

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

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
Genre of Resource
Conference Paper/ Presentation
Language
eng
Handle URL
https://hdl.handle.net/2142/121847

Owning Collections

PSAM 2023 Conference Proceedings PRIMARY