Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Uncertainty analysis of UAM TMI-1 benchmark by STREAM/RAST-K

  • Jaerim Jang (Advanced Reactor Technology Development Division, Korea Atomic Energy Research Institute) ;
  • Yunki Jo (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Deokjung Lee (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology)
  • Received : 2023.09.13
  • Accepted : 2023.12.02
  • Published : 2024.05.25
Download PDF
⟨ Previous Next ⟩

Abstract

This study rigorously examined uncertainty in the TMI-1 benchmark within the Uncertainty Analysis in Modeling (UAM) benchmark suite using the STREAM/RAST-K two-step method. It presents two pivotal advancements in computational techniques: (1) Development of an uncertainty quantification (UQ) module and a specialized library for the pin-based pointwise energy slowing-down method (PSM), and (2) Application of Principal Component Analysis (PCA) for UQ. To evaluate the new computational framework, we conducted verification tests using SCALE 6.2.2. Results demonstrated that STREAM's performance closely matched SCALE 6.2.2, with a negligible uncertainty discrepancy of ±0.0078% in TMI-1 pin cell calculations. To assess the reliability of the PSM covariance library, we performed verification tests, comparing calculations with Calvik's two-term rational approximation (EQ 2-term) covariance library. These calculations included both pin-based and fuel assembly (FA-wise) computations, encompassing hot zero-power and hot full-power operational conditions. The uncertainties calculated using both the EQ 2-term and PSM resonance treatments were consistent, showing a deviation within ±0.054%. Additionally, the data compression process yielded compression ratios of 88.210% and 92.926% for on-the-fly and data-saving approaches, respectively, in TMI fuel assembly calculations. In summary, this study provides a comprehensive explanation of the PCA process used for UQ calculations and offers valuable insights into the robustness and reliability of newly developed computational methods, supported by rigorous verification tests.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (No. NRF-2019M2D2A1A03058371).

References

  1. R.N. Bratton, M. Avramova, K. Ivanov, OECD/NEA benchmark for uncertainty analysis in modeling (UAM) for LWRS - summary and discussion of neutronics cases (phase I), Nucl. Eng. Technol. 46 (2014) 313-342, https://doi.org/10.5516/NET.01.2014.710. Organization for Economic Co-operation and Development. 
  2. S. Choi, W. Kim, J. Choe, W. Lee, H. Kim, B. Ebiwonjumi, E. Jeong, K. Kim, D. Yun, H. Lee, D. Lee, Development of high-fidelity neutron transport code STREAM, Comput. Phys. Commun. 264 (2021), https://doi.org/10.1016/j.cpc.2021.107915. 
  3. J. Park, J. Jang, H. Kim, J. Choe, D. Yun, P. Zhang, A. Cherezov, D. Lee, RAST-K v2-three-dimensional nodal diffusion code for pressurized water reactor core analysis, Energies 13 (2020) 6324, https://doi.org/10.3390/en13236324. 
  4. S. Choi, C. Lee, D. Lee, Resonance treatment using pin-based pointwise energy slowing-down method, J. Comp. Physiol. 330 (2017) 134-155, https://doi.org/10.1016/j.jcp.2016.11.007. 
  5. A. Cherezov, J. Jang, D. Lee, A PCA compression method for reactor core transient multiphysics simulation, Prog. Nucl. Energy 128 (2020), https://doi.org/10.1016/j.pnucene.2020.103441. 
  6. I.T. Jolliffe, J. Cadima, Principal component analysis: a review and recent developments, Philos. Trans. A Math. Phys. Eng. Sci. 374 (2016), 20150202, https://doi.org/10.1098/rsta.2015.0202. 
  7. B.T. Rearden, M.A. Jessee, Scale Code System, Oak Ridge National Laboratory, 2017. 
  8. M.B. Chadwick, M. Herman, P. Oblozinsky, M.E. Dunn, Y. Danon, A.C. Kahler, D. L. Smith, B. Pritychenko, G. Arbanas, R. Arcilla, R. Brewer, D.A. Brown, R. Capote, A.D. Carlson, Y.S. Cho, H. Derrien, K. Guber, G.M. Hale, S. Hoblit, S. Holloway, T. D. Johnson, T. Kawano, B.C. Kiedrowski, H. Kim, S. Kunieda, N.M. Larson, L. Leal, J.P. Lestone, R.C. Little, E.A. McCutchan, R.E. MacFarlane, M. MacInnes, C. M. Mattoon, R.D. McKnight, S.F. Mughabghab, G.P.A. Nobre, G. Palmiotti, A. Palumbo, M.T. Pigni, V.G. Pronyaev, R.O. Sayer, A.A. Sonzogni, N.C. Summers, P. Talou, I.J. Thompson, A. Trkov, R.L. Vogt, S.C. van der Marck, A. Wallner, M. C. White, D. Wiarda, P.G. Young, ENDF/B-VII.1 nuclear data for science and technology: cross sections, covariances, fission product yields and decay data, Nucl. Data Sheets 112 (2011) 2887-2996, https://doi.org/10.1016/j.nds.2011.11.002. 
  9. J. Jang, C. Kong, B. Ebiwonjumi, Y. Jo, D. Lee, Uncertainties of PWR spent nuclear fuel isotope inventory for back-end cycle analysis with Stream/RAST-K, Ann. Nucl. Energy 158 (2021), https://doi.org/10.1016/j.anucene.2021.108267. 
  10. J. Jang, C. Kong, B. Ebiwonjumi, A. Cherezov, Y. Jo, D. Lee, Uncertainty quantification in decay heat calculation of spent nuclear fuel by Stream/RAST-K, Nucl. Eng. Technol. 53 (2021) 2803-2815, https://doi.org/10.1016/j.net.2021.03.010. 
  11. L. Collin, xz." [Online], http://tukaani.org/xz/xz-5.0.7.tar.gz. 
  12. D. Tomatis, A. Dall'Osso, Compression of 3D pin-by-pin burnup data, Ann. Nucl. Energy 136 (2020), 107058, https://doi.org/10.1016/j.anucene.2019.107058. 
  13. X. Wu, C. Wang T, Kozlowski, kriging-based surrogate models for uncertainty quantification and sensitivity analysis, M&C, Jeju, Korea (2017). April 16-20. 
  14. R.J.J. Stamm'ler, M.J. Abbate, (LONDON) LTD, 1983, 0-12-663320-7. 
  15. D. Knott, A. Yamamoto, Lattice physics computations, in: D.G. Cacuci (Ed.), Handbook of Nuclear Engineering, Springer, 2010, pp. 913-1239. 
  16. J. Jang, Development of Nodal Diffusion Code for VVER and HTGR Analysis with Advanced Semi Analytic Nodal Method, Doctoral Thesis, Ulsan National Institute of Science and Technology, 2023. 
  17. J. Choe, S. Choi, P. Zhang, J. Park, W. Kim, H.C. Shin, H.S. Lee, J. Jung, D. Lee, Verification and validation of Stream/RAST-K for PWR analysis, Nucl. Eng. Technol. 51 (2019) 356-368, https://doi.org/10.1016/j.net.2018.10.004. 
  18. D.W. Muir, R.M. Boicourt, A.C. Kahler, J.L. Conlin, W. Haeck, The NJOY Nuclear Data Processing System, Version 2016, LA-UR-17-20093, Los Alamos National Laboratory, 2016. 
  19. M. Herman, A. Trkov, ENDF-6 formats manual, data formats and procedures for the evaluated nuclear data files ENDF/B-VI and ENDF/B-VII, National nuclear data center, Brookhaven National Laboratory, CSEWG Document ENDF-102, Report BNL-90365 Rev 1 (2009) 2010. 
  20. G. Chiba, ERRORJ: A Code to Process Neutron-Nuclide Reaction Cross Section Covariance, Japan Atomic Energy Agency, 2007 version 2.3, March. 
  21. V.C. Klema, A.J. Laub, The singular value decomposition: its computation and some applications, IEEE Trans. Automat. Control 25 (2) (1980). https://ieeexplore.ieee.org/document/1102314.  102314
  22. A. Yamamoto, K. Kinoshita, T. Watanabe, T. Endo, Y. Kodama, Y. Ohoka, T. Ushio, H. Nagano, Uncertainty quantification of LWR core characteristics using random sampling method, Nucl. Sci. Eng. 181 (2015) 160-174, https://doi.org/10.13182/NSE14-152. 
  23. V.C. Klema, A.J. Laub, The singular value decomposition: its computation and some applications, IEEE Trans. Automat. Control 25 (1980). https://ieeexplore.ieee.org/document/1102314.  102314
  24. HDF, User's guide HDF5, Release 1.10 (2019). The HDF Group, https://portal.hdfgroup.org/display/HDF5/HDF5+User+Guides?preview=/53610087/53610088/Users_Guide.pdf. 
  25. T. Zhu, Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes, Ecole ' Polytechnique F'ed'erale de Lausanne, 2015, https://doi.org/10.5075/epfl-thesis-6598. 
  26. N.J. Quartemont, A.A. Bickley, J.E. Bevins, Nuclear data covariance analysis in radiation-trransport simulations utilizing SCALE sampler and the IRDFF nuclear data library, IEEE Trans. Nucl. Sci. 67 (2020) 482-491. 
  27. K.A. Kinoshita, T. Yamamoto, Y. Endo, Y. Kodama, T. Ohoka, H. Ushio, Nagano, Uncertainty Quantification of Nutronics Characteristics Using Latin HyperCube Sampling Method, September 28, PHYSOR, Japan, 2014. Kyoto. 
  28. R.E. MacFarlane, Covariance plots for ENDF/B-VII.1. https://t2.lanl.gov/nis/data/endf/covVII.1/. Los Alamos National Laboratory. 
  29. E. Robert, MacFarlane, covariance plots for ENDF/B-VII.1, los alamos national laboratory. https://t2.lanl.gov/nis/data/endf/covVII.1. 
  30. K.M. Ivanov, S. Avramova, I. Kamerow, E. Kodeli, E. Sartori, O. Cabellos, Ivanov, Benchmark for Uncertainty Analysis in Modelling (Uam) for Design, Operation and Safety Analysis of LWRs, Volume I: Specification and Support Data for the Neutronics Cases (Phase I), OECD Nuclear Energy Agency, June 2016. 
  31. E. Canuti, A. Petruzzi, F. D'Auria, T. Kozlowski, Sensitivity studies for the exercise I-1 of the OECD/UAM benchmark, 2012, Science and Technology of Nuclear Installations (2012) 10, https://doi.org/10.1155/2012/817185. pages article ID 817185. 
  32. H.J. Park, McCARD/MIG stochastic sampling calculations for nuclear cross section sensitivity and uncertainty analysis, Nucl. Eng. Technol. 54 (2022) 4272-4279, https://doi.org/10.1016/j.net.2022.06.012. 
  33. A. Labarile, N. Olmo, R. Miro, ' T. Barrachina, G. Verdu, Comparison of SERPENT and SCALE methodology for LWRs transport calculations and additionally uncertainty analysis for cross-section perturbation with SAMPLER module, EPJ Nuclear Sci. Technol. 2 (2016), https://doi.org/10.1051/epjn/e2016-50002-7. 
  34. L. Mercatali, K. Ivanov, V.H. Sanchez, SCALE modeling of selected neutronics test problems within the OECD UAM LWR's benchmark, Sci. Technol. Nucl. Installations. (2013) 1-11, https://doi.org/10.1155/2013/573697, 2013. 
  35. C. Arenas, R. Bratton, F. Reventos, K. Ivanov, Uncertainty analysis of light water reactor fuel lattices, Sci. Technol. Nucl. Installations (2013) 1-10, https://doi.org/10.1155/2013/437409, 2013. 
  36. B.T. Rearden, M.L. Williams, M.A. Jessee, D.E. Mueller, D.A. Wiarda, Sensitivity and Uncertainty Analysis Capabilities and Data in SCALE, 174, 2011, https://doi.org/10.13182/NT174-236. 
  37. G. Ilas, H. Liljenfeldt, Decay heat uncertainty for BWR used fuel due to modeling and nuclear data uncertainties, Nucl. Eng. Des. 319 (2017) 176e184, https://doi.org/10.1016/j.nucengdes.2017.05.009. 
  38. NUREG, CR-6698, Guide for Validation of Nuclear Criticality Safety Calculational Methodology, Science Applications International Corporation, United States Nuclear Regulatory Commission, 2001. 
  39. O. Leray, D. Rochman, P. Grimm, H. Ferroukhi, A. Vasiliev, M. Hursin, G. Perret, A. Pautz, Nuclear data uncertainty propagation on spent fuel nuclide compositions, Ann. Nucl. Energy 94 (2016) 603-611, https://doi.org/10.1016/j.anucene.2016.03.023.