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Dynamic model uncertainty analysis and control system multi-objective optimization of space nuclear reactor
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DOI：10.1007/s41365-025-01710-7.png)
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Abstract
En 中文
Compared to other energy sources, nuclear reactors offer several advantages as a spacecraft power source, including compact size, high power density, and long operating life. These qualities make nuclear power an ideal energy source for future deep space exploration. A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics, reactivity control, reactor heat transfer, heat exchanger, and thermoelectric converter was developed. In addition, an electrical power control system was designed based on the developed dynamic model. The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics, thermal-hydraulics, and control system for the space reactor. The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters. The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation, with a relative standard deviation < 2.01%. Effective delayed neutron fraction was most sensitive to electrical power. To obtain optimal control performance, the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation. Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system, including 100% full power (FP) to 90% FP step load reduction transient and 5% FP/min linear variable load transient. The results showed that, considering the influence of system uncertainty, the optimized controller could improve the response speed and load following accuracy of electrical power control, in which the effectiveness and superiority have been verified.
Keywords:
Space nuclear reactor
Uncertainty quantification
Control system optimization
Sensitivity analysis
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