Groth, Katrina M., Denman, Matthew R., Cardoni, Jeffrey N., Wheeler, Timothy A.“Smart Procedures”: Using dynamic PRA to develop dynamic, context-specific severe accident management guidelines (SAMGs) (Inproceedings) Proceedings of the International Conference on Probabilistic Safety Assessment and Management (PSAM 12), Honolulu, HI, 2014.

BibTeX

@inproceedings{GrothPSAM2014_SAMGs,
title = {“Smart Procedures”: Using dynamic PRA to develop dynamic, context-specific severe accident management guidelines (SAMGs)},
author = {Katrina M Groth and Matthew R Denman and Jeffrey N Cardoni and Timothy A Wheeler},
year = {2014},
date = {2014-06-01},
booktitle = {Proceedings of the International Conference on Probabilistic Safety Assessment and Management (PSAM 12)},
address = {Honolulu, HI},
abstract = {Developing a big picture understanding of a severe accident is extremely challenging. Operating crews and emergency response teams are faced with rapidly evolving circumstances, uncertain information, distributed expertise, and a large number of conflicting goals and priorities. Severe accident management guidance (SAMGs) provides support for collecting information and assessing the state of a nuclear power plant during severe accidents. However, SAMGs developers cannot anticipate every possible accident scenario. Advanced Probabilistic Risk Assessment (PRA) methods can be used to explore an extensive space of possible accident sequences and consequences. Using this advanced PRA to develop a decision support system can provide expanded support for diagnosis and response. In this paper, we present an approach that uses dynamic PRA to develop risk-informed “Smart SAMGs”. Bayesian Networks form the basis of the faster-than-real-time decision support system. The approach leverages best-available information from plant physics simulation codes (e.g., MELCOR). Discrete Dynamic Event Trees (DDETs) are used to provide comprehensive coverage of the potential accident scenario space. This paper presents a methodology to develop Smart procedures and provides an example model created for diagnosing the status of the ECCS valves in a generic iPWR design.},
keywords = {Artificial intelligence, Bayesian Networks, Decision support systems, Dynamic PRA, nuclear power, risk-informed procedures, uncertainty},
pubstate = {published},
tppubtype = {inproceedings}
}

Abstract

Developing a big picture understanding of a severe accident is extremely challenging. Operating crews and emergency response teams are faced with rapidly evolving circumstances, uncertain information, distributed expertise, and a large number of conflicting goals and priorities. Severe accident management guidance (SAMGs) provides support for collecting information and assessing the state of a nuclear power plant during severe accidents. However, SAMGs developers cannot anticipate every possible accident scenario. Advanced Probabilistic Risk Assessment (PRA) methods can be used to explore an extensive space of possible accident sequences and consequences. Using this advanced PRA to develop a decision support system can provide expanded support for diagnosis and response. In this paper, we present an approach that uses dynamic PRA to develop risk-informed “Smart SAMGs”. Bayesian Networks form the basis of the faster-than-real-time decision support system. The approach leverages best-available information from plant physics simulation codes (e.g., MELCOR). Discrete Dynamic Event Trees (DDETs) are used to provide comprehensive coverage of the potential accident scenario space. This paper presents a methodology to develop Smart procedures and provides an example model created for diagnosing the status of the ECCS valves in a generic iPWR design.