RAVEN Beta Release

Cristian Rabiti; Andrea Alfonsi; Joshua Joseph Cogliati; Diego Mandelli; Robert Arthur Kinoshita; Congjian Wang; Daniel Patrick Maljovec; Paul William Talbot

doi:10.2172/1245532

RAVEN Beta Release

Cristian Rabiti
, Andrea Alfonsi
, Joshua Joseph Cogliati
, Diego Mandelli
, Robert Arthur Kinoshita
, Congjian Wang
, Daniel Patrick Maljovec
, Paul William Talbot

Research output: Book/Report › Technical Report

Abstract

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as 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. RAVEN has now increased in maturity enough for the Beta 1.0 release.

Original language	English
DOIs	https://doi.org/10.2172/1245532
State	Published - Feb 1 2016

Keywords

nuclear
optimization
PRA
safety
simulation
uncertainty quantification
MOOSE

INL Publication Number

INL/EXT-16-37953
29004

Access to Document

10.2172/1245532

https://www.osti.gov/servlets/purl/1245532

Cite this

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title = "RAVEN Beta Release",

abstract = "This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as 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. RAVEN has now increased in maturity enough for the Beta 1.0 release.",

keywords = "nuclear, optimization, PRA, safety, simulation, uncertainty quantification, MOOSE",

author = "Cristian Rabiti and Andrea Alfonsi and Cogliati, \{Joshua Joseph\} and Diego Mandelli and Kinoshita, \{Robert Arthur\} and Congjian Wang and Maljovec, \{Daniel Patrick\} and Talbot, \{Paul William\}",

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doi = "10.2172/1245532",

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AU - Rabiti, Cristian

AU - Alfonsi, Andrea

AU - Cogliati, Joshua Joseph

AU - Mandelli, Diego

AU - Kinoshita, Robert Arthur

AU - Wang, Congjian

AU - Maljovec, Daniel Patrick

AU - Talbot, Paul William

PY - 2016/2/1

Y1 - 2016/2/1

N2 - This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as 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. RAVEN has now increased in maturity enough for the Beta 1.0 release.

AB - This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as 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. RAVEN has now increased in maturity enough for the Beta 1.0 release.

KW - nuclear

KW - optimization

KW - PRA

KW - safety

KW - simulation

KW - uncertainty quantification

KW - MOOSE

UR - https://www.osti.gov/biblio/1245532

U2 - 10.2172/1245532

DO - 10.2172/1245532

M3 - Technical Report

BT - RAVEN Beta Release

ER -

RAVEN Beta Release

Abstract

Keywords

INL Publication Number

Access to Document

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