MINIMIZING CRUD DEPOSITION THROUGH OPTIMIZATION OF ASSOCIATED PARAMETERS

Abstract

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.

Keywords

• genetic algorithm
• machine learning
• crud
• optimization