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Selection of Sampling and Surrogate Modeling Methods for State-Point Evaluations of an AGN-201M Reactor

Journal Article · · Nuclear Science and Engineering
 [1];  [2];  [3];  [3];  [4];  [2];  [2]
  1. Oregon State Univ., Corvallis, OR (United States); Idaho National Laboratory (INL), Idaho Falls, ID (United States)
  2. Idaho National Laboratory (INL), Idaho Falls, ID (United States)
  3. Oregon State Univ., Corvallis, OR (United States)
  4. Idaho State Univ., Pocatello, ID (United States)
+ Show Author Affiliations
Nuclear reactor digital twins (DTs) have been proposed for use as a safeguards technology to efficiently monitor new and novel reactors as they come online. A safeguards DT needs to be capable of detecting misuse and diversion as they occur, requiring physics models to be accurate and efficient. Mathematical surrogate models are capable of achieving the necessary efficiency and can largely maintain the accuracy of higher-order models given a quality training sample. The Multiphysics Object-Oriented Simulation Environment (MOOSE) code framework is specifically equipped to generate training samples and create surrogate models using full-order reactor physics models. Utilizing an operational AGN-201M reactor’s specifications, two surrogate types were trained on samples of variable size, and using Cartesian products, Latin hypercube sampling, and quadrature sampling, each was compared and evaluated on accuracy when compared to a full-order Monte Carlo model. Both surrogate types were able to capture reactivity changes within 0.05 $ of the Monte Carlo model while reducing the computation costs by eight orders of magnitude.
Research Organization:
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE)
Grant/Contract Number:
AC07-05ID14517
OSTI ID:
2583267
Report Number(s):
INL/JOU--25-86453-Rev000
Journal Information:
Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering; ISSN 0029-5639; ISSN 1943-748X
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English

References (10)

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The Serpent Monte Carlo code: Status, development and applications in 2013 journal August 2015
Neutron transport methods for multiphysics heterogeneous reactor core simulation in Griffin journal June 2024
Digital Twins: State of the art theory and practice, challenges, and open research questions journal November 2022
Validation of the Griffin application for TREAT transient modeling and simulation journal December 2021
2.0 - MOOSE: Enabling massively parallel multiphysics simulation journal December 2022
MOOSE Stochastic Tools: A module for performing parallel, memory-efficient in situ stochastic simulations journal May 2023
Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code journal May 1979
Gaussian Processes for Machine Learning book January 2005

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Related Subjects

MOOSE
agn-201
digital twin
safeguards
surrogate model