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The objective of this study was to develop a unique
methodology and a practical tool for designing
loading pattern (LP) and burnable poison (BP)
pattern for a given Pressurized Water Reactor (PWR)
core. Because of the large number of possible
combinations for the fuel assembly (FA) loading in
the core, the design of the core configuration is a
complex optimization problem. It requires finding an
optimal FA arrangement and BP placement in order to
achieve maximum cycle length while satisfying the
safety constraints. To solve this optimization
problem, a LP optimization package, GARCO (Genetic
Algorithm Reactor Code Optimization) code is
developed. This code is applicable for all types of
PWR cores having different geometries and structures
with an unlimited number of FA types in the
inventory. To reach this goal, an innovative GA is
developed by modifying the classical representation
of the genotype. To obtain the best result in a
shorter time, not only the representation is changed
but also the algorithm is changed to use in-core
fuel management heuristics rules. The improved GA
code was tested to demonstrate and verify the
advantages of the new enhancements.
methodology and a practical tool for designing
loading pattern (LP) and burnable poison (BP)
pattern for a given Pressurized Water Reactor (PWR)
core. Because of the large number of possible
combinations for the fuel assembly (FA) loading in
the core, the design of the core configuration is a
complex optimization problem. It requires finding an
optimal FA arrangement and BP placement in order to
achieve maximum cycle length while satisfying the
safety constraints. To solve this optimization
problem, a LP optimization package, GARCO (Genetic
Algorithm Reactor Code Optimization) code is
developed. This code is applicable for all types of
PWR cores having different geometries and structures
with an unlimited number of FA types in the
inventory. To reach this goal, an innovative GA is
developed by modifying the classical representation
of the genotype. To obtain the best result in a
shorter time, not only the representation is changed
but also the algorithm is changed to use in-core
fuel management heuristics rules. The improved GA
code was tested to demonstrate and verify the
advantages of the new enhancements.