Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident

  • Zheng, Xiaoyu (Nuclear Safety Research Center, Japan Atomic Energy Agency) ;
  • Ishikawa, Jun (Nuclear Safety Research Center, Japan Atomic Energy Agency) ;
  • Sugiyama, Tomoyuki (Nuclear Safety Research Center, Japan Atomic Energy Agency) ;
  • Maruyama, Yu (Nuclear Safety Research Center, Japan Atomic Energy Agency)
  • Received : 2016.11.29
  • Accepted : 2016.12.26
  • Published : 2017.04.25
Download PDF
⟨ Previous Next ⟩

Abstract

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

Keywords

References

  1. R. Horst, P.M. Pardalos, N.V. Thoai, Introduction to Global Optimization, second ed., Springer Science & Business Media, New York, 2000.
  2. K. Kawaguchi, Y. Maruyama, X. Zheng, Global continuous optimization with error bound and fast convergence, J. Artif. Intell. Res. 56 (2016) 153-195. https://doi.org/10.1613/jair.4742
  3. B. Shahriari, K. Swesky, Z. Wang, R.P. Adams, N. de Freitas, Taking the human out of the loop: a review of Bayesian Optimization, Proc. IEEE 104 (2016) 48-175.
  4. R. Wachowiak, K. Canavan, Investigation of strategies for mitigating radiological releases in severe accidents: BWR Mark I and Mark II studies, EPRI technical report 1026539, Electric Power Research Institute, Palo Alto, California, USA, 2012. http://www.eenews.net/assets/2012/11/02/document_gw_02.pdf.
  5. E.J. Leeds, Issuance of order to modify licenses with regard to reliable hardened containment vents capable of operation under severe accident conditions, EA-13-109, U.S. Nuclear Regulatory Commission, Washington, D.C., USA, 2013. https://www.nrc.gov/docs/ML1314/ML13143A321.pdf.
  6. M. Kajimoto, K. Kuramatsu, N. Watanabe, M. Funasako, T. Noguchi, Development of THALES-2: a computer code for coupled thermal-hydraulics and fission product transport analyses for severe accident at LWRs and its application to analysis of fission product revaporization phenomena, in Proceedings of International Topical Meeting on Safety of Thermal Reactors, Portland, Oregon, July 21-25, 1991. http://jolissrch-inter.tokai-sc.jaea.go.jp/search/servlet/search?2056971.
  7. K. Moriyama, Y. Maruyama, H. Nakamura, Kiche: a simulation tool for kinetics of iodine chemistry in the containment of light water reactors under severe accident conditions (contact research), JAEA-Data/Code 2010-034, Japan Atomic Energy Agency, Ibaraki, Japan, 2011, http://jolissrch-inter.tokai-sc.jaea.go.jp/search/servlet/search?5028127.
  8. J. Ishikawa, K. Moriyama, Investigation for evaluating containment source term of BWR4/Mark-I plant considering iodine chemistry in suppression pool (in Japanese), JAEA-Research 2010-051, Japan Atomic Energy Agency, Ibaraki, Japan, 2010, http://jolissrch-inter.tokai-sc.jaea.go.jp/search/servlet/search?5027800.
  9. J. Ishikawa, K. Kawaguchi, Y. Maruyama, Analysis for iodine release from unit 3 of Fukushima Dai-ichi nuclear power plant with consideration of water phase iodine chemistry, J. Nucl. Sci. Technol. 52 (2015) 308-314. https://doi.org/10.1080/00223131.2014.951417
  10. B.R. Sehgal, Nuclear safety in light water reactors: severe accident phenomenology, Academic Press, Elsevier, Waltham, Massachusetts, 2012.
  11. J. Ishikawa, K. Muramatsu, T. Sakamoto, Systematic source term analysis for Level 3 PSA of a BWR with Mark-II type containment with THALES-2 code (in Japanese), JAERI-Research 2005-021, Japan Atomic Energy Research Institute, Ibaraki, Japan, 2005. http://jolissrch-inter.tokai-sc.jaea.go.jp/search/servlet/search?32447.
  12. J. Snoek, H. Larochelle, R.P. Adams, Practical Bayesian optimization of machine learning algorithms, Advances in Neural Information Processing Systems 25, Stateline, Nevada, USA, December 03-08, 2012. https://arxiv.org/abs/1206.2944.
  13. E. Brochu, V.M. Cora, N. de Freitas, A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, Tech. rep. UBC TR-2009-023 and arXiv:1012.2599, Department of Computer Science, University of British Columbia, Canada, 2009. https://arxiv.org/abs/1012.2599.
  14. Z. Wang, F. Hutter, M. Zoghi, D. Matheson, N. de Freitas, Bayesian optimization in a billion dimensions via random embeddings, J. Artif. Intell. Res. 55 (2016) 361-387. https://doi.org/10.1613/jair.4806
  15. A. O'Hagan, M.C. Kennedy, J.E. Oakley, Uncertainty analysis and other inference tools for complex computer codes, Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting, Oxford University Press, Oxford, 1999, pp. 503-524.
  16. J. Oakley, Bayesian uncertainty analysis for complex computer codes (Ph.D. dissertation), School of Mathematics and Statistics, the University of Sheffield, UK, 1999.
  17. C.E. Rasmussen, C.K.I. Williams, Gaussian processes for machine learning, The MIT Press, Cambridge, Massachusetts, 2006.
  18. R.M. Neal, Regression and classification using Gaussian process priors, Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting, Oxford University Press, Oxford, 1999, pp. 475-501.
  19. N. Girault, C. Fiche, A. Bujan, J. Dienstbier, Towards a better understanding of iodine chemistry in RCS of nuclear reactors, Nucl. Eng. Des. 239 (2009) 1162-1170. https://doi.org/10.1016/j.nucengdes.2009.02.008