Intelligent Optimization Method for Core Flow Zoning of Long-Cycle Lead-Bismuth-Cooled Reactor

Abstract

Flow zoning is an important way to achieve core outlet temperature flattening. Appropriate zoning can improve safety and economy. This study combines an artificial intelligence optimization algorithm with a parallel multi-channel model to develop a model for calculating reactor core flow zoning based on the modern optimization theory, convergence analysis of a genetic algorithm, differential evolution algorithm, and quantum genetic algorithm is carried out for long-life reactor flow partitioning. Using the optimized algorithm, two flow rates are determined using power distribution at the beginning of the core life as the sample data and the maximum power of each fuel assembly during the entire life as the sample data. Comparative analysis of two different flow zoning schemes is implemented on a small long-life natural circulation lead-bismuth fast reactor, SPALLER-100. The findings of this study show that the quantum genetic algorithm has the best convergence for the long-life reactor among the three intelligent optimization algorithms, and it can quickly provide optimal results. In flow zoning scheme calculations based on the core power distribution at the beginning of reactor life, the maximum outlet temperature of the fuel assembly exceeds the thermal safety limit of the reactor, and in the flow zoning scheme calculations based on the average core power distribution during the whole reactor life, the maximum outlet temperature of the fuel assembly is 140 K lower than the maximum outlet temperature obtained in the previous scheme, remaining below the thermal safety limit. The optimal number of partitions for the SPALLER-100 reactor is determined to be 5, and increasing the number of zones only slightly improved the thermal safety performance of the reactor.