Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

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]
+ Show Author Affiliations
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Research Org.:
Idaho National Laboratory (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.
Copy to clipboard
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. https://doi.org/10.1016/j.pnucene.2018.05.004
Copy to clipboard
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. https://doi.org/10.1016/j.pnucene.2018.05.004. https://www.osti.gov/servlets/purl/1478243.
Copy to clipboard
@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 = {Sat May 26 00:00:00 EDT 2018},
month = {Sat May 26 00:00:00 EDT 2018}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.pnucene.2018.05.004
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Citation Metrics:
Cited by: 10 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Hierarchical clustering: data set and corresponding dendrogram.
All figures and tables (17 total)
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

A Flooding Induced Station Blackout Analysis for a Pressurized Water Reactor Using the RISMC Toolkit
journal, January 2015

  • Mandelli, Diego; Prescott, Steven; Smith, Curtis
  • Science and Technology of Nuclear Installations, Vol. 2015
  • DOI: 10.1155/2015/308163

Reduced Order Modeling for Nonlinear Multi-Component Models
journal, January 2012

Dynamic event trees in accident sequence analysis: application to steam generator tube rupture
journal, January 1993

Mean shift: a robust approach toward feature space analysis
journal, May 2002

  • Comaniciu, D.; Meer, P.
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 5
  • DOI: 10.1109/34.1000236

Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
journal, July 2003

An approximate epistemic uncertainty analysis approach in the presence of epistemic and aleatory uncertainties
journal, September 2002

  • Hofer, Eduard; Kloos, Martina; Krzykacz-Hausmann, Bernard
  • Reliability Engineering & System Safety, Vol. 77, Issue 3
  • DOI: 10.1016/S0951-8320(02)00056-X

The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants
journal, June 1996

Hierarchical clustering schemes
journal, September 1967

Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study
journal, January 2016

BWR Station Blackout: A RISMC Analysis Using RAVEN and RELAP5-3D
journal, January 2016

  • Mandelli, D.; Smith, C.; Riley, T.
  • Nuclear Technology, Vol. 193, Issue 1
  • DOI: 10.13182/NT14-142

Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
journal, January 2009

    Sort by:
    [ × clear filter / sort ]

    Figures / Tables found in this record:

    Figure 1 (p. 7)figure
    Figure 2 (p. 8)figure
    Figure 3 (p. 9)figure
    Figure 4 (p. 11)figure
    Figure 5 (p. 11)figure
    Figure 6 (p. 12)figure
    Figure 7 (p. 14)figure
    Figure 8 (p. 15)figure
    Figure 9 (p. 16)figure
    Figure 10 (p. 17)figure
    Figure 11 (p. 18)figure
    Figure 12 (p. 18)figure
    Figure 13 (p. 19)figure
    Figure 14 (p. 20)figure
    Figure 15 (p. 21)figure
    Figure 16 (p. 22)figure
    Figure 17 (p. 23)figure
      [ × clear filter / sort ]
      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

      Similar Records in DOE PAGES and OSTI.GOV collections: