TY - GEN
T1 - Minimizing CRUD deposition through optimization of associated parameters
AU - Andersen, Brian
AU - Hou, Jason
AU - Kropaczek, Dave
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
PY - 2020
Y1 - 2020
N2 - A strong correlation exists between subcooled boiling in assembly subchannels and CRUD deposition. In this work a genetic algorithm is used to optimize a 17 x 17 PWR fuel assembly to have minimized subcooled boiling, minimized peak kinf, and maximized end of cycle kinf. Optimization of these parameters act as a surrogate for the optimization of CRUD deposition in fuel assemblies due to their strong correlation. Subcooled boiling, measured by vapor void in a sub channel, and values of kinf, are calculated using VERA-CS. Due to the high computational cost of VERA-CS, artificial neural networks are used as surrogate models to VERA-CS in order for the optimization to be performed in a timely manner making design work possible. Two neural networks were trained using a training library of 1200 randomly generated assembly designs and a validation library of 100 assembly designs evaluated using VERA-CS. The combination of neural networks and genetic algorithms formed an extremely fast optimization algorithm capable of evaluating designed a set of optimized pin lattices in a matter of minutes. The optimization showed a clear reduction in vapor void in the optimization solutions. This provides a proof of principle that complex phenomena requiring coupled, Multiphysics calculations, such as CRUD deposition, may be optimized.
AB - A strong correlation exists between subcooled boiling in assembly subchannels and CRUD deposition. In this work a genetic algorithm is used to optimize a 17 x 17 PWR fuel assembly to have minimized subcooled boiling, minimized peak kinf, and maximized end of cycle kinf. Optimization of these parameters act as a surrogate for the optimization of CRUD deposition in fuel assemblies due to their strong correlation. Subcooled boiling, measured by vapor void in a sub channel, and values of kinf, are calculated using VERA-CS. Due to the high computational cost of VERA-CS, artificial neural networks are used as surrogate models to VERA-CS in order for the optimization to be performed in a timely manner making design work possible. Two neural networks were trained using a training library of 1200 randomly generated assembly designs and a validation library of 100 assembly designs evaluated using VERA-CS. The combination of neural networks and genetic algorithms formed an extremely fast optimization algorithm capable of evaluating designed a set of optimized pin lattices in a matter of minutes. The optimization showed a clear reduction in vapor void in the optimization solutions. This provides a proof of principle that complex phenomena requiring coupled, Multiphysics calculations, such as CRUD deposition, may be optimized.
KW - CRUD
KW - Genetic algorithm
KW - Machine learning
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85108439926&partnerID=8YFLogxK
U2 - 10.1051/epjconf/202124712009
DO - 10.1051/epjconf/202124712009
M3 - Conference contribution
AN - SCOPUS:85108439926
T3 - International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020
SP - 2354
EP - 2361
BT - International Conference on Physics of Reactors
A2 - Margulis, Marat
A2 - Blaise, Partrick
PB - EDP Sciences - Web of Conferences
T2 - 2020 International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020
Y2 - 28 March 2020 through 2 April 2020
ER -