Parallel Computing Adaptive Simulated Annealing Scheme for Fuel Assembly Loading Pattern Optimization in PWRs

Abstract

An adaptive control scheme of simulated annealing (SA) parameters derived from the polynomial-time cooling schedule is presented in terms of the efficiency enhancement of the SA algorithm. The parallel computing adaptive SA optimization scheme, which incorporates the optimization-layer-by-layer (OLL) neutronics evaluation model is then applied to determining the optimum fuel assembly (FA) loading pattern (LP) in the Korea Nuclear Unit 2 pressurized water reactor (PWR) using seven Pentium personal computers (three 266-MHz Pentium II and four 200-MHz Pentium Pro computers). It is shown that the parallel scheme enhances the efficiency of the SA optimization computation significantly but that it can get trapped in local optimum LPs more frequently than the single-processor SA scheme unless one takes preventive steps. As a way to prevent trapping of the parallel scheme in local optima, using multiple seed LPs is proposed instead of a single LP with which the individual processors start each stage, and how to determine the multiple seed LPs is discussed. Because of the high efficiency of the parallel scheme, the acceptability of a hybrid neutronics evaluation model, which is slower but more accurate than the OLL model, in the parallel optimization calculation is examined from the standpoint of computing time. By demonstrating that the FA LP optimization calculation for the equilibrium cycle core of the KNU-2 PWR can be completed in <1 h on seven Pentiums, we justify the routine utilization of the hybrid model in the parallel SA optimization scheme.