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Title: Mining data in a dynamic PRA framework

Abstract

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1478243
Report Number(s):
INL/JOU-18-44526-Rev000
Journal ID: ISSN 0149-1970
Grant/Contract Number:  
AC07-05ID14517
Resource Type:
Accepted Manuscript
Journal Name:
Progress in Nuclear Energy
Additional Journal Information:
Journal Volume: 108; Journal Issue: C; Journal ID: ISSN 0149-1970
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Data mining; Dynamic PRA; Probabilistic risk assessment; Clustering

Citation Formats

Mandelli, Diego, Maljovec, Daniel, Alfonsi, Andrea, Parisi, Carlo, Talbot, Paul, Cogliati, Joshua J., Smith, Curtis, and Rabiti, Cristian. Mining data in a dynamic PRA framework. United States: N. p., 2018. Web. doi:10.1016/j.pnucene.2018.05.004.
Mandelli, Diego, Maljovec, Daniel, Alfonsi, Andrea, Parisi, Carlo, Talbot, Paul, Cogliati, Joshua J., Smith, Curtis, & Rabiti, Cristian. Mining data in a dynamic PRA framework. United States. doi:10.1016/j.pnucene.2018.05.004.
Mandelli, Diego, Maljovec, Daniel, Alfonsi, Andrea, Parisi, Carlo, Talbot, Paul, Cogliati, Joshua J., Smith, Curtis, and Rabiti, Cristian. Sat . "Mining data in a dynamic PRA framework". United States. doi:10.1016/j.pnucene.2018.05.004. https://www.osti.gov/servlets/purl/1478243.
@article{osti_1478243,
title = {Mining data in a dynamic PRA framework},
author = {Mandelli, Diego and Maljovec, Daniel and Alfonsi, Andrea and Parisi, Carlo and Talbot, Paul and Cogliati, Joshua J. and Smith, Curtis and Rabiti, Cristian},
abstractNote = {Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.},
doi = {10.1016/j.pnucene.2018.05.004},
journal = {Progress in Nuclear Energy},
number = C,
volume = 108,
place = {United States},
year = {2018},
month = {5}
}

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