Two stochastic optimization algorithms applied to nuclear reactor core design

Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications

Multilevel transport solution of LWR reactor cores

This work presents a multilevel approach for the solution of the transport equation in typical LWR assemblies and core configurations. It is based on the second-order, even-parity formulation of the transport equation, which is solved within the framework provided by the finite element-spherical harmonics code EVENT. The performance of the new solver has been compared with that of the standard conjugate gradient solver for diffusion and transport problems on structured and unstruc-tured grids. Numerical results demonstrate the potential of the multilevel scheme for realistic reactor calculations