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Title: RAVEN and Dynamic Probabilistic Risk Assessment: Software overview

Abstract

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7 [], currently under development at the Idaho National Laboratory. Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism has been employed by providing Application Programming Interfaces (APIs). These interfaces are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable to investigate the system response, investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The paper presents an overview of the software capabilities and their implementation schemes followed by some application examples.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
DOE - NE
OSTI Identifier:
1166032
Report Number(s):
INL/CON-14-31785
DOE Contract Number:  
DE-AC07-05ID14517
Resource Type:
Conference
Resource Relation:
Conference: European Safety and Reliability Conference ESREL,Wroclaw, Poland,09/14/2014,09/18/2014
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS; RAVEN; RELAP-7

Citation Formats

Alfonsi, Andrea, Rabiti, Cristian, Mandelli, Diego, Cogliati, Joshua, Kinoshita, Robert, and Naviglio, Antonio. RAVEN and Dynamic Probabilistic Risk Assessment: Software overview. United States: N. p., 2014. Web. doi:10.1201/b17399-106.
Alfonsi, Andrea, Rabiti, Cristian, Mandelli, Diego, Cogliati, Joshua, Kinoshita, Robert, & Naviglio, Antonio. RAVEN and Dynamic Probabilistic Risk Assessment: Software overview. United States. https://doi.org/10.1201/b17399-106
Alfonsi, Andrea, Rabiti, Cristian, Mandelli, Diego, Cogliati, Joshua, Kinoshita, Robert, and Naviglio, Antonio. 2014. "RAVEN and Dynamic Probabilistic Risk Assessment: Software overview". United States. https://doi.org/10.1201/b17399-106. https://www.osti.gov/servlets/purl/1166032.
@article{osti_1166032,
title = {RAVEN and Dynamic Probabilistic Risk Assessment: Software overview},
author = {Alfonsi, Andrea and Rabiti, Cristian and Mandelli, Diego and Cogliati, Joshua and Kinoshita, Robert and Naviglio, Antonio},
abstractNote = {RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7 [], currently under development at the Idaho National Laboratory. Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism has been employed by providing Application Programming Interfaces (APIs). These interfaces are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable to investigate the system response, investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The paper presents an overview of the software capabilities and their implementation schemes followed by some application examples.},
doi = {10.1201/b17399-106},
url = {https://www.osti.gov/biblio/1166032}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Sep 01 00:00:00 EDT 2014},
month = {Mon Sep 01 00:00:00 EDT 2014}
}

Conference:
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