DOI QR코드

DOI QR Code

The nuclear fuel cycle code ANICCA: Verification and a case study for the phase out of Belgian nuclear power with minor actinide transmutation

  • Rodriguez, I. Merino (SCK.CEN) ;
  • Hernandez-Solis, A. (SCK.CEN) ;
  • Messaoudi, N. (SCK.CEN) ;
  • Eynde, G. Van den (SCK.CEN)
  • 투고 : 2019.11.05
  • 심사 : 2020.04.04
  • 발행 : 2020.10.25

초록

The Nuclear Fuel Cycle Code "ANICCA" has been developed by SCK•CEN to answer particular questions about the Belgian nuclear fleet. However, the wide range of capabilities of the code make it also useful for international or regional studies that include advanced technologies and strategies of cycle. This paper shows the main features of the code and the facilities that can be simulated. Additionally, a comparison between several codes and ANICCA has also been made to verify the performance of the code by means of a simulation proposed in the last NEA (OECD) Benchmark Study. Finally, a case study of the Belgian nuclear fuel cycle phase out has been carried out to show the possible impact of the transmutation of the minor actinides on the nuclear waste by the use of an Accelerator Driven System also known as ADS. Results show that ANICCA accomplishes its main purpose of simulating the scenarios giving similar outcomes to other codes. Regarding the case study, results show a reduction of more than 60% of minor actinides in the Belgian nuclear cycle when using an ADS, reducing significantly the radiotoxicity and decay heat of the high-level waste and facilitating its management.

키워드

참고문헌

  1. B. Sjenitzer, Analysis of the Belgian nuclear fuel cycle using the ANICCA code, SCK.CEN External Report, SCK.CEN/ER-263 (2014).
  2. I. Merino, et al., ANICCA Code and the Belgian Nuclear Fuel Cycle, European Nuclear Conference (ENC), Warsaw (Poland), 2016, pp. 45-54. URL: https://www.euronuclear.org/events/enc/enc2016/transactions/ENC2016-transactions.pdf.
  3. A.V. Skarbeli, et al., Quantification of the differences introduced by nuclear fuel cycle simulators in advanced scenario studies, Ann. Nucl. Energy 137 (2020), https://doi.org/10.1016/j.anucene.2019.107160.
  4. C. Coquelet-Pascal, COSI6, A tool for nuclear transition scenario studies and application to SFR deployment scenarios with minor actinide transmutation, Nucl. Technol. 192 (2) (2015) 91-110, https://doi.org/10.13182/nt15-20.
  5. K.D. Huff, Fundamental concepts in the CYCLUS nuclear fuel cycle simulation framework, arXiv:1509.03604, Adv. Eng. Software 94 (2016) 46-59, https://doi.org/10.1016/j.advengsoft.2016.01.014. URL, http://arxiv.org/abs/1509.03604.
  6. A.M. Yacout, et al., Modeling the nuclear fuel cycle, in: The 23rd International Conference of the System Dynamics Society, Boston, MA, July 17-21, 2005.
  7. Nuclear Energy Agency, Benchmark Study on Nuclear Fuel Cycle Transition Scenarios Analysis Codes, vol. 16, NEA/NSC/WPFC/DOC, 2012, 2012. URL, https://www.oecd-nea.org/science/docs/2012/nsc-wpfc-doc2012-16.pdf330.
  8. R. Gregg, C. Grove, Analysis of the UK nuclear fission roadmap using the 28 ORION fuel cycle modeling code, in: Proceedings of the IChemE Nuclear Fuel Cycle Conference, Manchester, United Kingdom, 2012.
  9. A. Brolly, Physical model of the nuclear fuel cycle simulation code SITON, Ann. Nucl. Energy 99 (2017) 471-483, https://doi.org/10.1016/j.anucene.2016.10.001.
  10. I. Merino, et al., Cross check of the new economic and mass balance features of the fuel cycle scenario code TR_EVOL, EPJ Nucl. Sci. Technol. 2 (2016) 33, https://doi.org/10.1051/epjn/2016029.
  11. J.J. Jacobson, et al., Verifiable fuel cycle simulation model (VISION): a tool for analyzing nuclear fuel cycle futures, Nucl. Technol. 172 (2010) 157-178. November. https://doi.org/10.13182/NT172-157
  12. International Atomic Energy Agency, Nuclear Fuel Cycle Simulation System (VISTA), IAEA, TECDOC-1535.
  13. B. Feng, et al., Standardized verification of fuel cycle modeling, Ann. Nucl. Energy 94 (2016) 300-312, https://doi.org/10.1016/j.anucene.2016.03.002.
  14. J.W. Bae, et al., Standardized verification of the Cyclus fuel cycle simulator, Ann. Nucl. Energy 128 (2019) 288-291, https://doi.org/10.1016/j.anucene.2019.01.014.
  15. Nuclear Energy Agency, Nuclear fuel cycle transition scenario studies, status report: the Belgian implementation scenario, URL: https://www.oecd-nea.org/science/reports/2009/nea6194_transition_scenario_studies.pdf, 2009,978-92-64-99068-5.
  16. C. Artioli, et al., Optimization of the 365 minor actinides transmutation in ADS: the European Facility for Industrial Transmutation EFIT-Pb concept, in: Eighth International Topical Meeting on Nuclear Applications and Utilization of Accelerators, 2007. URL, https://www.tib.eu/en/search/id/TIBKAT%3A562259376/Eighth-International-Topical-Meeting-on-Nuclear.
  17. G. Van den Eynde, et al., An updated core design for the multi-purpose irradiation facility MYRRHA, J. Nucl. Sci. Technol. (52) (2015).
  18. A. Stankovskiy, G. Van den Eynde, Advanced method for calculations of core burn-up, activation of structural materials and spallation products accumulation in accelerator-driven systems, Sci. Technol. Nucl. Install. (2012) 1-12, https://doi.org/10.1155/2012/545103.
  19. A. Santamarina, et al., NEA, The JEFF-3.1.1 Nuclear Data Library. 370 Validation Results from JEF-2.2 to JEFF-3.1.1, vol. 6807, OECD, 2009, ISBN 978-92-64-99074-6. URL, https://www.oecd-nea.org/dbdata/nds_jefreports/jefreport-22/nea6807-jeff22.pdf.
  20. M. Pusa, Rational approximations to the matrix exponential in burnup calculations, Nucl. Sci. Eng. 169 (2011) 155-167. https://doi.org/10.13182/NSE10-81
  21. RED-IMPACT Synthesis Report, Forschungszentrum Julich (FZJ): Julich, FZJ, Germany, 2008, ISBN 978-3-89336-538-8.
  22. P. Baron, et al., A review of separation processes proposed for advanced fuel cycles based on technology readiness level assessments, Prog. Nucl. Energy 117 (2019) 103091. https://doi.org/10.1016/j.pnucene.2019.103091
  23. P. Diehl, et al., WISE uranium project, URL, www.wise-uranium.org.
  24. E. Supko, Parametric Study of Front-End Nuclear Fuel Cycle Costs Using Reprocessed Uranium, EPRI, Palo Alto, CA, 2010, p. 1020659.
  25. R. Baker, R.W. Ross, Comparison of the value of plutonium and uranium isotopes in fast reactors, in: Proc. Conf. Breeding, Economics, and Safety in Large Fast Breeder Reactors. ANL-679Z, 1963.
  26. K.O. Ott, R.C. Borg, Derivation of consistent measures for the doubling time of fast breeder reactor fuel, Nucl. Sci. Eng. 62 (1977) 243. https://doi.org/10.13182/NSE77-A26960
  27. EG-AFCS, Working party on scientific issues of the fuel cycle, URL, https://www.oecd-nea.org/science/wpfc/index_afcs.html.
  28. C.J. Werner, et al., MCNP Users Manual - Code Version 6.2, Los Alamos National Laboratory, 2017. LA-UR-17-29981.
  29. V. Tsibulskiy, et al., DESAE (Dynamic energy system- atomic energy) integrated computer model for performing global analysis in INPRO assessment studies, in: International Conference on Nuclear Engineering "ICONE 14" 17-20 July, 2006 (Miami, Florida, USA).
  30. International Atomic Energy Agency, The Power Reactor Information System (PRIS) and its Extension to Non-electrical Applications, Decommissioning and Delayed Projects Information, IAEA, VIENNA, 2005. Technical Reports Series No.428.
  31. FANC, Second Meeting of the Contracting Parties to the Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management, Federal Agency for Nuclear Control, Kingdom of Belgium, May 2006. National Report.
  32. C.H. Clement, ICRP publication 119: compendium of dose coefficients based on ICRP publication 60, Ann. ICRP 42 (4) (2013).

피인용 문헌

  1. Development of a MOX equivalence Python code package for ANICCA vol.7, 2020, https://doi.org/10.1051/epjn/2021023