Optimization of plutonium and minor actinide transmutation in an AP1000 fuel assembly via a genetic search algorithm

The average nuclear power plant produces twenty metric tons of used nuclear fuel per year, which contains approximately 95 wt% uranium, 1 wt% plutonium, and 4 wt% fission products and transuranic elements. Fast reactors are the preferred option for the transmutation of plutonium and minor actinides; however, an optimistic deployment time of at least 20 years indicates a need for a near-term solution. Previous simulation work demonstrated the potential to transmute transuranic elements in a modified light water reactor fuel pin. This study optimizes a quarter-assembly containing target fuels coated with spectral shift absorbers for the transmutation of plutonium and minor actinides in light water reactors. The spectral shift absorber coating on the target fuel pin tunes the neutron energy spectrum experienced by the target fuel. A coupled model developed using the NEWT module from SCALE 6.1 and a genetic algorithm module from the DAKOTA optimization toolbox provided performance data for the burnup of the target fuel pins in the present study. The optimization with the coupled NEWT/DAKOTA model proceeded in three stages. The first stage optimized a single-target fuel pin per quarter-assembly adjacent to the central instrumentation channel. The second stage evaluated a variety of quarter-assemblies with multiple target fuel pins from the first stage and the third stage re-optimized the pins in the optimal second stage quarter-assembly. An 8 wt% PuZrO2MgO inert matrix fuel pin with a 1.44 mm radius and a 0.06 mm Lu2O3 coating in a five target fuel pin per quarter-assembly configuration represents the optimal combination for the transmutation of plutonium and minor actinides in the LWR environment considered in this study.

[1]  T. H. Fanning,et al.  Separations and Transmutation Criteria to Improve Utilization of a Geologic Repository , 2006 .

[2]  A. Galperin.  Utilization of light water reactors for plutonium incineration , 1995 .

[3]  J. King,et al.  Target fuels for plutonium and minor actinide transmutation in pressurized water reactors , 2017 .

[4]  O. H. Zabunoğlu,et al.  Determination of fissile fraction in MOX (mixed U + Pu oxides) fuels for different burnup values , 2011 .

[5]  Kostadin Ivanov,et al.  Development of enriched Gd-155 and Gd-157 burnable poison designs for a PWR core , 2006 .

[6]  F. Khoshahval,et al.  An enhanced integer coded genetic algorithm to optimize PWRs , 2011 .

[7]  Philip R. Page,et al.  ENDF/B-VII.0: Next Generation Evaluated Nuclear Data Library for Nuclear Science and Technology , 2006 .

[8]  M. Aghaie,et al.  Advanced progressive real coded genetic algorithm for nuclear system availability optimization through preventive maintenance scheduling , 2013 .

[9]  Kostadin Ivanov,et al.  Application of genetic algorithms to optimize burnable poison placement in pressurized water reactors , 2006 .

[10]  M. Salvatores,et al.  Radioactive waste partitioning and transmutation within advanced fuel cycles: Achievements and Challenges , 2011 .

[11]  J. King,et al.  Selection and evaluation of potential burnable absorbers incorporated into modified TRISO particles , 2014 .

[12]  E. Zio,et al.  Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification , 2009 .

[13]  Ephraim Nissan,et al.  FuelGen: a genetic algorithm-based system for fuel loading pattern design in nuclear power reactors , 1998 .

[14]  J. Duderstadt,et al.  Nuclear reactor analysis , 1976 .

[15]  Kostadin Ivanov,et al.  New genetic algorithms (GA) to optimize PWR reactors: Part I: Loading pattern and burnable poison placement optimization techniques for PWRs , 2008 .

[16]  Safety and neutronics: A comparison of MOX vs UO2 fuel , 2006 .

[17]  Ehud Greenspan,et al.  Uranium–zirconium hydride fuel properties , 2009 .

[18]  Wagner F. Sacco,et al.  A parallel genetic algorithm with niching technique applied to a nuclear reactor core design optimization problem , 2008 .

[19]  Xue Dazhi,et al.  A genetic algorithm solution for a nuclear power plant risk–cost maintenance model , 2004 .

[20]  Kemper Lewis,et al.  Effective generation of Pareto sets using genetic programming , 2001 .

[21]  R. C. Weast.  CRC Handbook of Chemistry and Physics , 1973 .

[22]  Celso Marcelo Franklin Lapa,et al.  Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem , 2003 .

[23]  T. L. Schulz,et al.  Westinghouse AP1000 advanced passive plant , 2006 .