Hybrid mechanistic and neural network modeling of nuclear reactors

Abstract

An accurate and efficient model is the foundation for dynamic characteristics analysis and control design of nuclear reactors. Nevertheless, the widely-used mechanistic models of nuclear reactors inevitably have some deviations from the actual reactors due to inaccurate model parameters and model assumptions. This paper proposes a hybrid mechanistic and neural network modeling method for nuclear reactors to eliminate or minimizing these deviations. The high-efficiency point reactor kinetics model was adopted as the mechanistic reactor core model, and the simulation results obtained using the commercial program RELAP5 were taken as the reference operational data, based on which the reactivity feedback in the mechanistic model that is difficult to be accurately determined during reactor operations was calibrated using a genetic algorithm optimized BP neural network. Dynamic simulation results of a Generation III large pressured water reactor under three typical reactivity change transients show that the core power and outlet coolant temperature obtained with the hybrid model match much better with the reference results than those of the mechanistic model, demonstrating that the hybrid modeling method can improve the accuracy of the mechanistic reactor model effectively. This study can provide a valuable reference for accurate and efficient modeling of complex systems.