An Online State Estimation Method Based on Quantum Genetic Algorithm for Space Nuclear Reactors

Abstract Space nuclear reactors provide continuous, stable, and powerful energy supply for long-term space exploration missions, their operation being independent of sunlight. However, the experimental data of space reactors is very limited, and there is significant uncertainty in numerical models based on traditional numerical algorithms or machine learning. Therefore, it is necessary to develop an online state estimation algorithm to obtain the true operating status of space reactors. This paper introduces an online state estimation method for space nuclear reactors, designed to mitigate the limitations in accuracy and generalizability associated with machine learning-enhanced models. During the operation phase of the reactor, the machine learning accelerated model remains parallel to the real reactor in time. By using sensors and other measuring devices, the reactor transmits measurement data to the model within a certain time interval. After receiving measurement parameters, a quantum genetic algorithm is used for parameter optimization, adjusting critical coefficients in the model to align computational results with actual measurements, thereby more accurately simulating the real operating state. Critical coefficient determination involves a two-step process: First, a preliminary array of critical coefficients is selected through sensitivity analysis of program parameters; Secondly, recursive methods are used to analyze the sensitivity of these critical coefficients to model modifications and determine the final array of critical coefficients. The study focuses on the improved TOPAZ-II model with heat pipes, employing a traditional numerical algorithm-based model as a benchmark. The results show that the proposed method can effectively correct the results of the machine learning-enhanced model, and the corrected model can more accurately reflect the operating state of the reactor.