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Title: ADAPTIVE SURROGATES WITHIN THE RAVEN FRAMEWORK FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT ANALYSIS

Abstract

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment (PRA), Uncertainty Quantification (UQ), data mining analysis and optimization studies. RAVEN has demonstrated a good level of maturity in terms of state-of-art and advanced analysis methodologies. In the past year the RAVEN code has been released as open-source project and is available to download (raven.inl.gov) free of charge. The main subject of this paper is to present a new system within the RAVEN framework to drastically accelerate PRA and UQ analysis using acceleration schemes based on surrogate modeling. This system is based on the heavy usage of surrogate modeling and artificial intelligence algorithms, able to emulate the response of a physical system in closed domains (domains where the surrogate models have been constructed from). The aim of this paper is to show to the community how the employment of surrogate modeling techniques can be a viable solution for the current and future challenges in terms of PRA, UQ and Safety Margins characterizations. In addition, in this manuscript it is presented the construction of a RAVENmore » new entity named “HybridModel” that is aimed to switch between a high-fidelity code and its linked surrogate, accelerating the PRA and UQ processes without largely reducing the accuracy of the results of the analyses. All these new capabilities will be shown with an application example about a Station Black Out scenario for a prototypical Pressurized Water Reactor (modeled with RELAP5-3D).« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Idaho National Laboratory
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1478770
Report Number(s):
INL/CON-18-44391-Rev000
DOE Contract Number:  
AC07-05ID14517
Resource Type:
Conference
Resource Relation:
Conference: BEPU2018, Lucca, Italy, 05/13/2018 - 05/19/2018
Country of Publication:
United States
Language:
English
Subject:
97 - MATHEMATICS AND COMPUTING; RAVEN; SURROGATE MODELS; RELAP5-3D; PRA; UQ

Citation Formats

Alfonsi, A., Rabiti, C., Wang, C., and Mandelli, D. ADAPTIVE SURROGATES WITHIN THE RAVEN FRAMEWORK FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT ANALYSIS. United States: N. p., 2018. Web.
Alfonsi, A., Rabiti, C., Wang, C., & Mandelli, D. ADAPTIVE SURROGATES WITHIN THE RAVEN FRAMEWORK FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT ANALYSIS. United States.
Alfonsi, A., Rabiti, C., Wang, C., and Mandelli, D. Tue . "ADAPTIVE SURROGATES WITHIN THE RAVEN FRAMEWORK FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT ANALYSIS". United States. https://www.osti.gov/servlets/purl/1478770.
@article{osti_1478770,
title = {ADAPTIVE SURROGATES WITHIN THE RAVEN FRAMEWORK FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT ANALYSIS},
author = {Alfonsi, A. and Rabiti, C. and Wang, C. and Mandelli, D.},
abstractNote = {The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment (PRA), Uncertainty Quantification (UQ), data mining analysis and optimization studies. RAVEN has demonstrated a good level of maturity in terms of state-of-art and advanced analysis methodologies. In the past year the RAVEN code has been released as open-source project and is available to download (raven.inl.gov) free of charge. The main subject of this paper is to present a new system within the RAVEN framework to drastically accelerate PRA and UQ analysis using acceleration schemes based on surrogate modeling. This system is based on the heavy usage of surrogate modeling and artificial intelligence algorithms, able to emulate the response of a physical system in closed domains (domains where the surrogate models have been constructed from). The aim of this paper is to show to the community how the employment of surrogate modeling techniques can be a viable solution for the current and future challenges in terms of PRA, UQ and Safety Margins characterizations. In addition, in this manuscript it is presented the construction of a RAVEN new entity named “HybridModel” that is aimed to switch between a high-fidelity code and its linked surrogate, accelerating the PRA and UQ processes without largely reducing the accuracy of the results of the analyses. All these new capabilities will be shown with an application example about a Station Black Out scenario for a prototypical Pressurized Water Reactor (modeled with RELAP5-3D).},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

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