Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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A methodology for uncertainty quantification and sensitivity analysis for responses subject to Monte Carlo uncertainty with application to fuel plate characteristics in the ATRC

  • Received : 2021.06.23
  • Accepted : 2021.09.08
  • Published : 2022.03.25
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Abstract

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of keff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for keff uncertainty and the Monte Carlo uncertainty in calculated keff values can have a large effect on computed linear model parameters for parameters with low influence on keff.

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Acknowledgement

This manuscript has in part been authored by Battelle Energy Alliance,LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the paper for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Also, this research made use of the resources of the High Performance Computing Center at Idaho National Laboratory, which is supported by the Office of Nuclear Energy of the U.S. Department of Energy and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517. The authors would also like to acknowledge Daniel Kelly and Scott Spychala as well as the Naval Nuclear Laboratory for their help in code/model development and technical support as well as Ryan Little from the ATR facility. We would also like to thank Majdi Radaideh from the Massachusetts Institute of Technology for the valuable discussions on sensitivity analysis methods we had in the early stages of this work.

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