Fatih Alim

Heuristic rules embedded genetic algorithm to solve in-core fuel management optimization problem

Because of the large number of possible combinations for the fuel assembly loading in the core, the design of the loading pattern (LP) is a complex optimization problem. It requires finding an optimal fuel arrangement in order to achieve maximum cycle length while satisfying the safety constraints. The objective of this study is to develop a loading pattern optimization code. Generally in-core fuel management codes are written for specific cores and limited fuel inventory. One of the goals of this study is to develop a loading pattern optimization code, which is applicable for all types of Pressurized Water Reactor (PWR) core structures with unlimited number of fuel assembly types in the inventory. To reach this goal an innovative genetic algorithm is developed with 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 demonstrating the advantages of the introduced enhancements. The core physics code used in this research is Moby-Dick, which was developed to analyze the VVER reactors by SKODA Inc.