New genetic algorithms (GA) to optimize PWR reactors: Part I: Loading pattern and burnable poison placement optimization techniques for PWRs

Abstract

The objective of this study was to develop a unique scientific methodology as well as a practical tool for designing the 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 corresponding BP placement design that will achieve maximum cycle length while satisfying the safety constraints. To solve this optimization problem, a core reload 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 designs with an unlimited number of FA types in the inventory. GARCO has three modes: the user can optimize the core configuration (LP pattern) with or without BPs in the first mode; the second mode is the optimization of BP placement in the core and the last mode is the user can optimize LP and BP placements simultaneously in mode 3. In this study, the first mode finds the optimal LPs using the Haling Power Depletion Method (HPD) for placing BPs in the core. The second mode, which depletes the core accurately, places BPs in the selected optimum LP pattern. This methodology is applied only to the TMI-1 PWR. However, the improved Mode 1 GA option was applied to both the VVER-1000 and the TMI-1 to demonstrate and verify the advantages of the new enhancements in optimizing the LP pattern only. The “Moby-Dick” code is used as reactor physics code for VVER-1000 analysis in this research. The SIMULATE-3 code, which is an advanced two-group nodal code, is used to analyze the TMI-1. The libraries of the BP designs used in SIMULATE-3 in this study were produced by Yilmaz (2005) [Yilmaz, S., 2005. Multilevel optimization of burnable poison utilization for advanced PWR fuel management. Ph.D. Thesis in Nuclear Engineering. the Pennsylvania State University].