skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit

Abstract

In the past decades, several numerical simulation codes have been employed to simulate accident dynamics (e.g., RELAP5-3D, RELAP-7, MELCOR, MAAP). In order to evaluate the impact of uncertainties into accident dynamics, several stochastic methodologies have been coupled with these codes. These stochastic methods range from classical Monte-Carlo and Latin Hypercube sampling to stochastic polynomial methods. Similar approaches have been introduced into the risk and safety community where stochastic methods (such as RAVEN, ADAPT, MCDET, ADS) have been coupled with safety analysis codes in order to evaluate the safety impact of timing and sequencing of events. These approaches are usually called Dynamic PRA or simulation-based PRA methods. These uncertainties and safety methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). The scope of this paper is to present a broad overview of methods and algorithms that can be used 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. Some of the algorithms presented here have been developed or are under development within the RAVEN statistical framework.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [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:
1364493
Report Number(s):
INL/EXT-16-39851
M3LW-17IN0704083; TRN: US1703393
DOE Contract Number:
AC07-05ID14517
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; COMPUTERIZED SIMULATION; SAFETY MARGINS; SAFETY ANALYSIS; DATA ANALYSIS; STOCHASTIC PROCESSES; ALGORITHMS; HAZARDS; MONTE CARLO METHOD; ACCIDENTS; clustering; data mining; PRA

Citation Formats

Mandelli, Diego, Alfonsi, Andrea, Maljovec, Daniel P., Parisi, Carlo, Cogliati, Joshua J., Talbot, Paul W., Smith, Curtis L., Rabiti, Cristian, and Picoco, Claudia. Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit. United States: N. p., 2016. Web. doi:10.2172/1364493.
Mandelli, Diego, Alfonsi, Andrea, Maljovec, Daniel P., Parisi, Carlo, Cogliati, Joshua J., Talbot, Paul W., Smith, Curtis L., Rabiti, Cristian, & Picoco, Claudia. Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit. United States. doi:10.2172/1364493.
Mandelli, Diego, Alfonsi, Andrea, Maljovec, Daniel P., Parisi, Carlo, Cogliati, Joshua J., Talbot, Paul W., Smith, Curtis L., Rabiti, Cristian, and Picoco, Claudia. Thu . "Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit". United States. doi:10.2172/1364493. https://www.osti.gov/servlets/purl/1364493.
@article{osti_1364493,
title = {Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit},
author = {Mandelli, Diego and Alfonsi, Andrea and Maljovec, Daniel P. and Parisi, Carlo and Cogliati, Joshua J. and Talbot, Paul W. and Smith, Curtis L. and Rabiti, Cristian and Picoco, Claudia},
abstractNote = {In the past decades, several numerical simulation codes have been employed to simulate accident dynamics (e.g., RELAP5-3D, RELAP-7, MELCOR, MAAP). In order to evaluate the impact of uncertainties into accident dynamics, several stochastic methodologies have been coupled with these codes. These stochastic methods range from classical Monte-Carlo and Latin Hypercube sampling to stochastic polynomial methods. Similar approaches have been introduced into the risk and safety community where stochastic methods (such as RAVEN, ADAPT, MCDET, ADS) have been coupled with safety analysis codes in order to evaluate the safety impact of timing and sequencing of events. These approaches are usually called Dynamic PRA or simulation-based PRA methods. These uncertainties and safety methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). The scope of this paper is to present a broad overview of methods and algorithms that can be used 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. Some of the algorithms presented here have been developed or are under development within the RAVEN statistical framework.},
doi = {10.2172/1364493},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 2016},
month = {Thu Sep 01 00:00:00 EDT 2016}
}

Technical Report:

Save / Share: