The Greedy Exhaustive Dual Binary Swap methodology for fuel loading optimization in PWR reactors using the poropy reactor optimization Tool

Abstract

This thesis presents the development and analysis of a deterministic optimization scheme termed Greedy Exhaustive Dual Binary Swap for the optimization of nuclear reactor core loading patterns. The goal of this optimization scheme is to emulate the approach taken by an engineer when manually optimizing a reactor core loading pattern. This is to determine if this approach is able to locate high quality patterns that, due to their location in the core loading solution space, are consistently missed by standard stochastic optimization methods such as those in the genetic algorithm class, or those in the simulated annealing class. This optimization study is carried out using the poropy tool to handle the reactor physics model. Initially, optimizations are carried out using beginning of cycle eigenvalue as a surrogate for core excess reactivity and thus cycle length. The deterministic Dual Binary Swap is found to locate acceptable patterns less reliably than stochastic methods, but those that are located are of higher quality. Optimizations of the full depletion problem result in the deterministic Dual Binary Swap optimizer locating patterns that are of higher quality than those found by the stochastic Simulated Annealing, with comparable frequency. The Dual Binary Swap optimizer is, however, found to be very dependent on the starting core configuration, and can not reliably find a high quality pattern from any given starting configuration.