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Title: Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors

Abstract

The optimization of a Sodium-cooled Fast Reactor (SFR) core is a challenging process, due to the large number of design parameters, the nonlinearities among inputs and outputs, and the complicated correlation among output parameters. This study attempts to develop a generalized framework for the SFR core optimization by coupling the sensitivity analysis, advanced optimization algorithm, and optionally the surrogate modeling. The framework is built based on the fast reactor modeling capability of the Argonne Reactor Computation (ARC) suite and the sensitivity analysis and optimization modules embedded in the DAKOTA code, both have been integrated within the NEAMS Workbench. The genetic algorithm is selected as the optimization method for its robustness, while the option of surrogate modeling is also explored to alleviate the computational burden caused by employing the ARC direct core physics simulation and thus enhance the efficiency of the optimization. Finally, the normalized deviations of performance parameters of the near-optimal solution from those of the ideal core are calculated and used as criteria to down select the final core design. The developed framework is applied to the Advanced Burner Test Reactor (ABTR) core, and optimal solutions are determined by balancing various objectives simultaneously, including peak fast flux, core volume,more » power, reactivity swing, plutonium mass feed, while at the same time satisfying the predefined constraints due to safety or economics considerations. The optimal ABTR core design obtained using the direct physical simulation and surrogated model are compared and discussed. It is found that using the accurately constructed surrogate models can significantly reduce the required computational time while maintaining satisfactory accuracy.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3];  [2]
+ Show Author Affiliations
  1. North Carolina State Univ., Raleigh, NC (United States); Argonne National Lab. (ANL), Argonne, IL (United States). Nuclear Engineering Division
  2. Argonne National Lab. (ANL), Argonne, IL (United States). Nuclear Engineering Division
  3. North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1606518
Alternate Identifier(s):
OSTI ID: 1682457
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Progress in Nuclear Energy
Additional Journal Information:
Journal Volume: 120; Journal Issue: C; Journal ID: ISSN 0149-1970
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; SFR; genetic algorithm; multi-objective optimization; sensitivity analysis; test reactor

Citation Formats

Zeng, Kaiyue, Stauff, Nicolas E., Hou, Jason, and Kim, Taek K. Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors. United States: N. p., 2019. Web. doi:10.1016/j.pnucene.2019.103184.
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Zeng, Kaiyue, Stauff, Nicolas E., Hou, Jason, & Kim, Taek K. Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors. United States. https://doi.org/10.1016/j.pnucene.2019.103184
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Zeng, Kaiyue, Stauff, Nicolas E., Hou, Jason, and Kim, Taek K. Thu . "Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors". United States. https://doi.org/10.1016/j.pnucene.2019.103184. https://www.osti.gov/servlets/purl/1606518.
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@article{osti_1606518,
title = {Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors},
author = {Zeng, Kaiyue and Stauff, Nicolas E. and Hou, Jason and Kim, Taek K.},
abstractNote = {The optimization of a Sodium-cooled Fast Reactor (SFR) core is a challenging process, due to the large number of design parameters, the nonlinearities among inputs and outputs, and the complicated correlation among output parameters. This study attempts to develop a generalized framework for the SFR core optimization by coupling the sensitivity analysis, advanced optimization algorithm, and optionally the surrogate modeling. The framework is built based on the fast reactor modeling capability of the Argonne Reactor Computation (ARC) suite and the sensitivity analysis and optimization modules embedded in the DAKOTA code, both have been integrated within the NEAMS Workbench. The genetic algorithm is selected as the optimization method for its robustness, while the option of surrogate modeling is also explored to alleviate the computational burden caused by employing the ARC direct core physics simulation and thus enhance the efficiency of the optimization. Finally, the normalized deviations of performance parameters of the near-optimal solution from those of the ideal core are calculated and used as criteria to down select the final core design. The developed framework is applied to the Advanced Burner Test Reactor (ABTR) core, and optimal solutions are determined by balancing various objectives simultaneously, including peak fast flux, core volume, power, reactivity swing, plutonium mass feed, while at the same time satisfying the predefined constraints due to safety or economics considerations. The optimal ABTR core design obtained using the direct physical simulation and surrogated model are compared and discussed. It is found that using the accurately constructed surrogate models can significantly reduce the required computational time while maintaining satisfactory accuracy.},
doi = {10.1016/j.pnucene.2019.103184},
journal = {Progress in Nuclear Energy},
number = C,
volume = 120,
place = {United States},
year = {Thu Nov 14 00:00:00 EST 2019},
month = {Thu Nov 14 00:00:00 EST 2019}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1016/j.pnucene.2019.103184
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Works referenced in this record:

New genetic algorithms (GA) to optimize PWR reactors
journal, January 2008

New genetic algorithms (GA) to optimize PWR reactors
journal, January 2008

New genetic algorithms (GA) to optimize PWR reactors
journal, January 2008

Feasibility Study of a Micro Modular Reactor for Military Ground Applications
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  • S. Allen, Kenneth; K. Hartford, Samuel; J. Merkel, Gregory
  • Journal of Defense Management, Vol. 08, Issue 01
  • DOI: 10.4172/2167-0374.1000172

ENDF/B-VII.0: Next Generation Evaluated Nuclear Data Library for Nuclear Science and Technology
journal, December 2006

Surrogates based multi-criteria predesign methodology of Sodium-cooled Fast Reactor cores – Application to CFV-like cores
journal, August 2016

A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis
journal, November 2015

Various approaches in optimization of a typical pressurized water reactor power plant
journal, July 2009

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