Sensitivity analysis, surrogate modeling, and optimization of pebble-bed reactors considering normal and accident conditions

Prince, Zachary M.; Balestra, Paolo; Ortensi, Javier; Schunert, Sebastian; Calvin, Olin; Hanophy, Joshua T.; Mo, Kun; Strydom, Gerhard

doi:10.1016/j.nucengdes.2024.113466

Sensitivity analysis, surrogate modeling, and optimization of pebble-bed reactors considering normal and accident conditions

Journal Article · Mon Jul 22 00:00:00 EDT 2024 · Nuclear Engineering and Design

DOI:https://doi.org/10.1016/j.nucengdes.2024.113466· OSTI ID:2432417

^[1]; ^[1]; ^[1]; ^[1]; ^[1]; Hanophy, Joshua T. ^[1]; ; Strydom, Gerhard ^[1]

Idaho National Laboratory (INL), Idaho Falls, ID (United States)

This research provides a valuable tool that streamlines the optimization process while significantly increasing its accuracy. This study creates a robust framework for reactor design optimization by incorporating comprehensive modeling using the Comprehensive Reactor Analysis Bundle, or BlueCRAB, within the Multiphysics Object-Oriented Simulation Environment (MOOSE). BlueCRAB is the United States Nuclear Regulatory Commission's code suite for non-light water reactor analysis and includes the Griffin, Pronghorn, and Bison applications. This not only improves the efficiency of the optimization process but also enhances the reliability of the results. Such a tool is essential for advancing the state-of-the-art in pebble-bed reactor technology and is critical for achieving the goals of Generation IV reactors, which aim for safe, sustainable, and economically viable nuclear energy solutions. This work presents and applies this workflow on pebble-bed reactors while considering both normal and off-normal conditions. A representative gas-cooled pebble-bed reactor at equilibrium core conditions serves as the nominal design specification for normal operation and is based on previous research. The depressurized loss-of-forced-cooling accident is deployed for off-normal conditions in this work. After defining design-related parameters and quantities of interest regarding reactor safety and performance, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of surrogate models. Subsequently, the dataset and surrogate models are employed in two optimization studies aimed at maximizing fuel utilization and economic profit while adhering to safety and operational constraints. Performing the optimization process with fuel utilization as the metric leads to an improvement of approximately 10%, compared to engineering-judgment-based nominal conditions. The optimization on economic profit leads to an estimated increase of ~300 million USD over the lifetime of the reactor.

View Accepted Manuscript (DOE)

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:: 2432417

Report Number(s):: INL/JOU--23-74718-Rev000

Journal Information:: Nuclear Engineering and Design, Journal Name: Nuclear Engineering and Design Journal Issue: - Vol. 428; ISSN 0029-5493

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (37)

A study of the differential scheme for composite materials McLaughlin, R. International Journal of Engineering Science, Vol. 15, Issue 4 https://doi.org/10.1016/0020-7225(77)90058-1	journal	January 1977
Making best use of model evaluations to compute sensitivity indices Saltelli, Andrea Computer Physics Communications, Vol. 145, Issue 2 https://doi.org/10.1016/S0010-4655(02)00280-1	journal	May 2002
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates Sobol′, I. M. Mathematics and Computers in Simulation, Vol. 55, Issue 1-3 https://doi.org/10.1016/S0378-4754(00)00270-6	journal	February 2001
In-core fuel management optimization of pebble-bed reactors Boer, B.; Kloosterman, J. L.; Lathouwers, D. Annals of Nuclear Energy, Vol. 36, Issue 8 https://doi.org/10.1016/j.anucene.2009.06.008	journal	August 2009
The Serpent Monte Carlo code: Status, development and applications in 2013 Leppänen, Jaakko; Pusa, Maria; Viitanen, Tuomas Annals of Nuclear Energy, Vol. 82 https://doi.org/10.1016/j.anucene.2014.08.024	journal	August 2015
Physics-based multiscale coupling for full core nuclear reactor simulation Gaston, Derek R.; Permann, Cody J.; Peterson, John W. Annals of Nuclear Energy, Vol. 84 https://doi.org/10.1016/j.anucene.2014.09.060	journal	October 2015
Cumulative migration method for computing rigorous diffusion coefficients and transport cross sections from Monte Carlo Liu, Zhaoyuan; Smith, Kord; Forget, Benoit Annals of Nuclear Energy, Vol. 112 https://doi.org/10.1016/j.anucene.2017.10.039	journal	February 2018
Study on the DLOFC and PLOFC accidents of the 200 MWe pebble-bed modular high temperature gas-cooled reactor with TINTE and SPECTRA codes Zheng, Yanhua; Stempniewicz, Marek M.; Chen, Zhipeng Annals of Nuclear Energy, Vol. 120 https://doi.org/10.1016/j.anucene.2018.06.041	journal	October 2018
Generation of localized reactor point kinetics parameters using coupled neutronic and thermal fluid models for pebble-bed reactor transient analysis Stewart, Ryan; Balestra, Paolo; Reger, David Annals of Nuclear Energy, Vol. 174 https://doi.org/10.1016/j.anucene.2022.109143	journal	September 2022
Improved multiphysics model of the High Temperature Engineering Test Reactor for the simulation of loss-of-forced-cooling experiments Labouré, Vincent; Ortensi, Javier; Martin, Nicolas Annals of Nuclear Energy, Vol. 189 https://doi.org/10.1016/j.anucene.2023.109838	journal	September 2023
Discrete element simulation of Pebble Bed Reactors on graphics processing units Reger, David; Merzari, Elia; Balestra, Paolo Annals of Nuclear Energy, Vol. 190 https://doi.org/10.1016/j.anucene.2023.109896	journal	September 2023
An equilibrium core depletion algorithm for pebble-bed reactors in the Griffin code Schunert, Sebastian; Ortensi, Javier; Wang, Yaqi Annals of Nuclear Energy, Vol. 192 https://doi.org/10.1016/j.anucene.2023.109980	journal	November 2023
Depletion chain simplification using pseudo-nuclides Calvin, Olin Annals of Nuclear Energy, Vol. 193 https://doi.org/10.1016/j.anucene.2023.110011	journal	July 2023
Least squares polynomial chaos expansion: A review of sampling strategies Hadigol, Mohammad; Doostan, Alireza Computer Methods in Applied Mechanics and Engineering, Vol. 332 https://doi.org/10.1016/j.cma.2017.12.019	journal	April 2018
ENDF/B-VIII.0: The 8 th Major Release of the Nuclear Reaction Data Library with CIELO-project Cross Sections, New Standards and Thermal Scattering Data Brown, D. A.; Chadwick, M. B.; Capote, R. Nuclear Data Sheets, Vol. 148 https://doi.org/10.1016/j.nds.2018.02.001	journal	February 2018
Preliminary reactor physics assessment of the HTR module with 14% enriched UCO fuel Strydom, Gerhard; Gougar, Hans D. Nuclear Engineering and Design, Vol. 256, 304-321 https://doi.org/10.1016/j.nucengdes.2012.08.013	journal	October 2012
Optimisation of deep burn incineration of reactor waste plutonium in a PBMR DPP-400 core Serfontein, Dawid E.; Mulder, Eben J.; Reitsma, Frederik Nuclear Engineering and Design, Vol. 271 https://doi.org/10.1016/j.nucengdes.2013.11.017	journal	May 2014
Pebble bed reactor fuel cycle optimization using particle swarm algorithm Tavron, Barak; Shwageraus, Eugene Nuclear Engineering and Design, Vol. 307 https://doi.org/10.1016/j.nucengdes.2016.06.033	journal	October 2016
A control approach investigation of the Xe-100 plant to perform load following within the operational range of 100 – 25 – 100% Brits, Yvotte; Botha, Frikkie; van Antwerpen, Herman Nuclear Engineering and Design, Vol. 329 https://doi.org/10.1016/j.nucengdes.2017.11.041	journal	April 2018
Neutronics characteristics of a 165 MWth Xe-100 reactor Mulder, E. J.; Boyes, W. A. Nuclear Engineering and Design, Vol. 357 https://doi.org/10.1016/j.nucengdes.2019.110415	journal	February 2020
FEA-aided investigation of the effective thermal conductivity in a medium with embedded spheres Toptan, Aysenur; Jiang, Wen; Hales, Jason D. Nuclear Engineering and Design, Vol. 381 https://doi.org/10.1016/j.nucengdes.2021.111355	journal	September 2021
A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers Stewart, Ryan H.; Palmer, Todd S.; DuPont, Bryony Progress in Nuclear Energy, Vol. 138 https://doi.org/10.1016/j.pnucene.2021.103830	journal	August 2021
An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis Blatman, Géraud; Sudret, Bruno Probabilistic Engineering Mechanics, Vol. 25, Issue 2 https://doi.org/10.1016/j.probengmech.2009.10.003	journal	April 2010
Modelling using Polynomial Regression Ostertagová, Eva Procedia Engineering, Vol. 48 https://doi.org/10.1016/j.proeng.2012.09.545	journal	January 2012
Global sensitivity analysis using polynomial chaos expansions Sudret, Bruno Reliability Engineering & System Safety, Vol. 93, Issue 7 https://doi.org/10.1016/j.ress.2007.04.002	journal	July 2008
2.0 - MOOSE: Enabling massively parallel multiphysics simulation Lindsay, Alexander D.; Gaston, Derek R.; Permann, Cody J. SoftwareX, Vol. 20 https://doi.org/10.1016/j.softx.2022.101202	journal	December 2022
MOOSE Stochastic Tools: A module for performing parallel, memory-efficient in situ stochastic simulations Slaughter, Andrew E.; Prince, Zachary M.; German, Peter SoftwareX, Vol. 22 https://doi.org/10.1016/j.softx.2023.101345	journal	May 2023
Pbmr-400 Benchmark Solution of Exercise 1 and 2 Using the Moose Based Applications: Mammoth, Pronghorn Balestra, Paolo; Schunert, Sebastian; Carlsen, Robert W. EPJ Web of Conferences, Vol. 247 https://doi.org/10.1051/epjconf/202124706020	journal	January 2021
Pronghorn: A Multidimensional Coarse-Mesh Application for Advanced Reactor Thermal Hydraulics Novak, A. J.; Carlsen, R. W.; Schunert, S. Nuclear Technology https://doi.org/10.1080/00295450.2020.1825307	journal	February 2021
BISON: A Flexible Code for Advanced Simulation of the Performance of Multiple Nuclear Fuel Forms Williamson, Richard L.; Hales, Jason D.; Novascone, Stephen R. Nuclear Technology https://doi.org/10.1080/00295450.2020.1836940	journal	March 2021
Rattlesnake: A MOOSE-Based Multiphysics Multischeme Radiation Transport Application Wang, Yaqi; Schunert, Sebastian; Ortensi, Javier Nuclear Technology, Vol. 207, Issue 7 https://doi.org/10.1080/00295450.2020.1843348	journal	April 2021
Factorial Sampling Plans for Preliminary Computational Experiments Morris, Max D. Technometrics, Vol. 33, Issue 2 https://doi.org/10.1080/00401706.1991.10484804	journal	May 1991
Physical Analysis of the Initial Core and Running-In Phase for Pebble-Bed Reactor HTR-PM Zhang, Jingyu; Li, Fu; Sun, Yuliang Science and Technology of Nuclear Installations, Vol. 2017 https://doi.org/10.1155/2017/8918424	journal	January 2017
Approximation and learning by greedy algorithms Barron, Andrew R.; Cohen, Albert; Dahmen, Wolfgang The Annals of Statistics, Vol. 36, Issue 1 https://doi.org/10.1214/009053607000000631	journal	February 2008
Automated Design and Optimization of Pebble-Bed Reactor Cores Gougar, H. D.; Ougouag, A. M.; Terry, W. K. Nuclear Science and Engineering, Vol. 165, Issue 3 https://doi.org/10.13182/NSE08-89	journal	July 2010
Scattering Reduction of the Double-Heterogeneity Treatment in Dragon Hébert, A. Nuclear Science and Engineering, Vol. 160, Issue 2 https://doi.org/10.13182/NSE160-261TN	journal	October 2008
Initial Study on Cross-Section Generation Requirements for a PBR Equilibrium Core Ortensi, Javier; Balestra, Paolo International Conference on Physics of Reactors 2022 (PHYSOR 2022) https://doi.org/10.13182/PHYSOR22-37527	conference	January 2022

Similar Records

Sensitivity Analysis, Reduced-order Modeling, and Optimization of a Gas-Cooled Pebble Bed Reactor using Equilibrium-Core and DLOFC Performance

S&T Accomplishment Report · Thu Aug 31 20:00:00 EDT 2023 · OSTI ID:3018309

Gas-Cooled High-Temperature Pebble-Bed Reactor Reference Plant Model

Technical Report · Fri Mar 31 20:00:00 EDT 2023 · OSTI ID:1984239

Fluoride-Cooled High-Temperature Pebble-Bed Reactor Reference Plant Model

Technical Report · Sun Apr 30 20:00:00 EDT 2023 · OSTI ID:1983953

Related Subjects

97 MATHEMATICS AND COMPUTING
Depressurized loss of forced cooling
Equilibrium core
MOOSE
Optimization
Pebble bed reactor
Sensitivity analysis
Surrogate modeling

Sensitivity analysis, surrogate modeling, and optimization of pebble-bed reactors considering normal and accident conditions

Citation Formats

References (37)

Similar Records

Related Subjects