Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Surrogate based model calibration for pressurized water reactor physics calculations

  • Khuwaileh, Bassam A. (Department of Nuclear Engineering, North Carolina State University) ;
  • Turinsky, Paul J. (Department of Nuclear Engineering, North Carolina State University)
  • Received : 2017.06.04
  • Accepted : 2017.08.07
  • Published : 2017.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this work, a scalable algorithm for model calibration in nuclear engineering applications is presented and tested. The algorithm relies on the construction of surrogate models to replace the original model within the region of interest. These surrogate models can be constructed efficiently via reduced order modeling and subspace analysis. Once constructed, these surrogate models can be used to perform computationally expensive mathematical analyses. This work proposes a surrogate based model calibration algorithm. The proposed algorithm is used to calibrate various neutronics and thermal-hydraulics parameters. The virtual environment for reactor applications-core simulator (VERA-CS) is used to simulate a three-dimensional core depletion problem. The proposed algorithm is then used to construct a reduced order model (a surrogate) which is then used in a Bayesian approach to calibrate the neutronics and thermal-hydraulics parameters. The algorithm is tested and the benefits of data assimilation and calibration are highlighted in an uncertainty quantification study and requantification after the calibration process. Results showed that the proposed algorithm could help to reduce the uncertainty in key reactor attributes based on experimental and operational data.

Keywords

References

  1. W.C. Proctor, Elements of High-order Predictive Model Calibration Algorithms with Applications to Large-scale Reactor Physics Systems, North Carolina State University, 2012.
  2. J.M. Hite, H.S. Abdel-Khalik, R.C. Smith, M. Wentworth, E. Prudencio, B. Williams, Uncertainty Quantification and Data Assimilation (UQ/DA) Study on a VERA Core Simulator Component for CRUD Analysis. CASL-I-2013-0184-000, 2013.
  3. R.C. Smith, Uncertainty Quantification: Theory, Implementation, and Applications, vol. 12, SIAM, 2013.
  4. H. Haario, M. Laine, A. Mira, E. Saksman, DRAM: efficient adaptive MCMC, Stat. Comput. 16 (2006) 339-354. https://doi.org/10.1007/s11222-006-9438-0
  5. H. Haario, E. Saksman, J. Tamminen, An adaptive Metropolis algorithm, Bernoulli 7 (2001) 223-242. https://doi.org/10.2307/3318737
  6. B.A.A. Khuwaileh, Scalable Methods for Uncertainty Quantification, Data Assimilation and Target Accuracy Assessment for Multi-Physics Advanced Simulation of Light Water Reactors, ProQuest, 2015.
  7. A. Godfrey, VERA Core Physics Benchmark Progression Problem Specifications, Revision 4, CASL Technical Report: CASL-U-2012-0131-004, August 29, 2014.
  8. N. Halko, P. Martinsson, J.A. Tropp, Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, SIAM Review 53 (2011) 217-288. https://doi.org/10.1137/090771806
  9. B.M. Adams, M.S. Ebeida, S. Michael, M.S. Eldred, J.D. Jakeman, L.P. Swiler, J.A. Stephens, D.M. Vigil, T.M. Wildey, W.J. Bohnhoff, K.R. Dalbey, J.P. Eddy, K.T. Hu, L.E. Bauman, P.D. Hough, DAKOTA, a Multilevel Parallel Objectoriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.1 User's Manual, Sandia National Laboratories, 2014. Tech. Rep. SAND2014-4633.
  10. B.A. Khuwaileh, R. Hooper, P.J. Turinsky, V.A. Mousseau, Uncertainty Quantification Analysis Using VERA-CS for a PWR Fuel Assembly with Depletion. CASL-I-2015-0328-000, 2015.

Cited by

  1. Non-linear, time dependent target accuracy assessment algorithm for multi-physics, high dimensional nuclear reactor calculations vol.114, pp.None, 2019, https://doi.org/10.1016/j.pnucene.2019.01.023
  2. Gaussian process approach for dose mapping in radiation fields vol.52, pp.8, 2020, https://doi.org/10.1016/j.net.2020.01.013
  3. Differential parameters uncertainty estimation via a PCA-based monte carlo sampling approach: IRT-4M fuel type as a case study vol.58, pp.9, 2017, https://doi.org/10.1080/00223131.2021.1899996