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.
Highlights
The deposition of CRUD, with the backronym Chalk River Unidentified deposits, is a serious concern for the operating fleet of light water reactors, as they are pushed towards higher burnups
The objectives for the optimization were to minimize the total amount of vapor void produced in the assembly and maximum kinf within the assembly over a 20 GWD/MTU depletion, while maximizing the EOC kinf value of the assembly using the pin types listed in Table III as the decision variable
High fidelity Multiphysics codes such as Virtual Environment for Reactor Analysis Core Simulator (VERA-CS) cannot be used for design work due to the high computational cost involved
Summary
The deposition of CRUD, with the backronym Chalk River Unidentified deposits, is a serious concern for the operating fleet of light water reactors, as they are pushed towards higher burnups. Multiphysics simulation tools such as the Virtual Environment for Reactor Analysis Core Simulator (VERA-CS) allows for coupling of codes such as MPACT and CTF to provide threedimensional pin level detail of parameters correlated to CRUD deposition such as sub channel vapor void. Sub-channel vapor void is chosen as an optimization parameter based on the strong correlation between subcooled boiling and CRUD deposition in PWR fuel assemblies. This makes this work a proof of concept for future assembly optimization of CRUD deposition. VERA-CS, while extremely powerful in regard to its simulation capabilities, is expensive in terms of wall clock time and computational resources This makes it unsuited for use in an optimization algorithm. Genetic algorithms are used as the optimization methodology [7]
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