Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Verification of Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE)

  • Khuwaileh, Bassam (Department of Mechanical and Nuclear Engineering, University of Sharjah) ;
  • Williams, Brian (Los Alamos National Laboratory) ;
  • Turinsky, Paul (Department of Nuclear Engineering, North Caroline State University) ;
  • Hartanto, Donny (Department of Mechanical and Nuclear Engineering, University of Sharjah)
  • Received : 2018.11.06
  • Accepted : 2019.01.30
  • Published : 2019.05.25
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Abstract

This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

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References

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