Research on Intelligent Optimization Method for Core Flow Zoning of Lead-bismuth Reactor

Core flow zoning is an important means to achieve core outlet temperature flattening. Reasonable zoning can improve the reactor safety and economy. In this paper, the artificial intelligence optimization algorithm is coupled with the single channel model, and the calculation model of reactor core flow zoning is constructed. The convergence analysis of genetic algorithm, differential evolution algorithm and quantum genetic algorithm in reactor flow zoning is carried out respectively. According to the obtained optimal algorithm, taking the power distribution at the beginning of life cycle as the sample data and the maximum power of each fuel assembly throughout the life cycle as the sample data, the comparative analysis of two different flow zoning schemes is carried out based on the small long-life natural circulation lead-bismuth fast reactor SPALLER -100. The results show that among the three intelligent optimization algorithms, the quantum genetic algorithm has the best convergence on the reactor flow zoning problem, and can quickly search the optimal zoning results; The maximum outlet temperature of the fuel assembly based on the power distribution at the beginning of the life cycle exceeds the thermal safety limit of the reactor, while the maximum outlet temperature of the fuel assembly based on the maximum power of each fuel assembly during the entire life by 140 K and remains below the thermal safety limit; The optimal number of zones for SPALLER-100 reactor is 5, and increasing the number of zones has little effect on improving the thermal safety performance of the reactor.