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SARAPAN-A Simulated-Annealing-Based Tool to Generate Random Patterned-Channel-Age in CANDU Fuel Management Analyses

  • Kastanya, Doddy (Safety and Licensing Department, Candesco Division of Kinectrics Inc.)
  • Received : 2016.05.16
  • Accepted : 2016.07.18
  • Published : 2017.02.25

Abstract

In any reactor physics analysis, the instantaneous power distribution in the core can be calculated when the actual bundle-wise burnup distribution is known. Considering the fact that CANDU (Canada Deuterium Uranium) utilizes on-power refueling to compensate for the reduction of reactivity due to fuel burnup, in the CANDU fuel management analysis, snapshots of power and burnup distributions can be obtained by simulating and tracking the reactor operation over an extended period using various tools such as the $^*SIMULATE$ module of the Reactor Fueling Simulation Program (RFSP) code. However, for some studies, such as an evaluation of a conceptual design of a next-generation CANDU reactor, the preferred approach to obtain a snapshot of the power distribution in the core is based on the patterned-channel-age model implemented in the $^*INSTANTAN$ module of the RFSP code. The objective of this approach is to obtain a representative snapshot of core conditions quickly. At present, such patterns could be generated by using a program called RANDIS, which is implemented within the $^*INSTANTAN$ module. In this work, we present an alternative approach to derive the patterned-channel-age model where a simulated-annealing-based algorithm is used to find such patterns, which produce reasonable power distributions.

Keywords

References

  1. W. Shen, P. Schwanke, Evolution of RFSP 3.5 for CANDU Analysis, in: Proceeding to the 33rd Annual Conference of the Canadian Nuclear Society, Saskatoon, SK, Canada, June 2012.
  2. B. Rouben, RFSP-IST, the industry standard tool computer program for CANDU reactor core design and analysis, in: Proceedings of the 13th Pacific Basin Nuclear Conference, Shenzhen, China, October 21-25, 2002.
  3. H. Choi, G. Roh, Benchmarking MCNP and WIMS/RFSP against measurement data-I: deuterium critical assembly, Nucl. Sci. Eng. 146 (2005) 188-199.
  4. H. Choi, G. Roh, D. Park, Benchmarking MCNP and WIMS/RFSP against measurement data-II: Wolsong nuclear power plant 2, Nucl. Sci. Eng. 150 (2005) 37-55. https://doi.org/10.13182/NSE05-A2500
  5. J.J. Song, Fuelling Study of a CANDU Reactor Using Fuel Containing Burnable Neutron Absorbers, Master of Applied Science thesis, Royal Military College of Canada, Kingston, Canada, 2015.
  6. S. Kirkpatrik, C. Gelatt, M. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671-680. https://doi.org/10.1126/science.220.4598.671
  7. D.J. Kropaczek, P.J. Turinsky, In-core nuclear fuel management optimization for pressurized water reactor utilizing simulated annealing, Nucl. Technol. 95 (1991) 9-32. https://doi.org/10.13182/NT95-1-9
  8. Y.P. Mahler, Core reload optimization for equilibrium cycles using simulated annealing and successive linear programming, Ann. Nucl. Energy 29 (2002) 1327-1344. https://doi.org/10.1016/S0306-4549(01)00113-X
  9. A.H. Fadaei, S. Setayeshi, LONSA as a tool for loading pattern optimization for VVER-1000 using synergy of a neural network and simulated annealing, Ann. Nucl. Energy 35 (2008) 1968-1973. https://doi.org/10.1016/j.anucene.2008.05.001
  10. H. Hernandez, G.I. Maldonado, Application of simulated annealing optimization to recycle minor actinides in a BWR lattice, Trans. Am. Nucl. Soc. 96 (2007) 771-773.
  11. B.R. Moore, P.J. Turinsky, A.A. Karve, FORMOSA-B: a boiling water reactor in-core fuel management optimization package, Nucl. Technol. 126 (1999) 153-169. https://doi.org/10.13182/NT99-A2964
  12. P.D. Buchan, C. Stewart, Application of simulated annealing to determining RFSP target exit irradiations, in: Proceeding of CNS 23rd Nuclear Simulation Symposium, Ottawa, Ontario, 2008.