https://doi.org/10.1051/epjconf/202124712009
MINIMIZING CRUD DEPOSITION THROUGH OPTIMIZATION OF ASSOCIATED PARAMETERS
1 North Carolina State University 2500 Stinson Drive. Raleigh NC,
2 Oak Ridge National Laboratory One Bethel Valley Road, Bldg. 5700, MS-6003, Oak Ridge. TN, 37830,
Bdander3@ncsu.edu
jason.hou@ncsu.edu
kropaczekdj@ornl.gov
Published online: 22 February 2021
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.
Key words: Genetic Algorithm / Machine Learning / CRUD / Optimization
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
