HEURISTIC RULES IN GENETIC ALGORITHM FOR FUEL MANAGEMENT OPTIMIZATION

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.

Autorentext

Dr. Alim was born in 1975 in Turkey. He graduated from Department of Nuclear Engineering at Hacettepe University in 1997. He earned his Ph.D. degree in Nuclear and Mechanical Engineering from Penn State University in 2006. He is currently working in Turkish Atomic Energy Authority as a Nuclear Engineer. He is married and has a daughter.

Klappentext

The objective of this study was to develop a uniquemethodology and a practical tool for designingloading pattern (LP) and burnable poison (BP)pattern for a given Pressurized Water Reactor (PWR)core. Because of the large number of possiblecombinations for the fuel assembly (FA) loading inthe core, the design of the core configuration is acomplex optimization problem. It requires finding anoptimal FA arrangement and BP placement in order toachieve maximum cycle length while satisfying thesafety constraints. To solve this optimizationproblem, a LP optimization package, GARCO (GeneticAlgorithm Reactor Code Optimization) code isdeveloped. This code is applicable for all types ofPWR cores having different geometries and structureswith an unlimited number of FA types in theinventory. To reach this goal, an innovative GA isdeveloped by modifying the classical representationof the genotype. To obtain the best result in ashorter time, not only the representation is changedbut also the algorithm is changed to use in-corefuel management heuristics rules. The improved GAcode was tested to demonstrate and verify theadvantages of the new enhancements.