Design and assessment of a core-power controller for lithium-cooled space nuclear reactor based on the concept of fuzzy model predictive control

Abstract

Thanks to its unique characteristics of high power-to-mass ratio, shallow reactivity poisoning, and quick response to reactivity control, power supply system based on lithium-cooled space nuclear reactor is preferred for various exploration missions into outer and deep space. However, due to its nature of few-people or even unmanned on-duty, an intelligent autonomous control of the reactor system, especially an accurate control of the reactor core power following the demanding power output, is of vital importance for such a space nuclear reactor. In this study, a core-power controller for a megawatt ultra-small lithium-cooled space nuclear reactor was designed based on the concept of fuzzy model predictive control (FMPC) combining model predictive control and T-S fuzzy theory. Performance of the FMPC controller was simulated and assessed with the Simulink platform for five typical operation transients including ramp, step and disturbance transient. The results show that the intelligent FMPC controller possesses an excellent load-following ability and anti-interference ability, both of which are of vital importance for space exploration missions. When compared with the classical PID controller, the FMPC controller designed in this study shows also a much better performance with smaller overshoot, lesser adjusting time and lower integral time-squared error.

Preparation of HMn2O4 lithium-ion sieves with low manganese dissolution loss for improved cycling stability

“…Owing to the strong competition in the new energy field 1,2 and bright prospects in the nuclear energy field, 3 the demand for lithium is high and continues to grow. 4 Although it is currently mainly extracted from ores, the rapid depletion of nonrenewable ores calls for an urgent need to draw on seawater and salt lake brine resources, which account for around 60% of the global reserves.…”

A decision framework for orderly power utilization based on a computationally enhanced algorithm

“…In order to obtain the OPU plan for the next day, the decision maker needs to forecast the load/generation information for an entire contract period (for example, 1 month or half a year) and finish OPU optimization. Take 1 month as an example, only the OPU plan of the second day will be adopted and other plans that are further away will be only used as a reference and will not be implemented, which is similar to the idea of the model predictive control method (Huang et al, 2023;Yin et al, 2023). Taking the entire contract cycle into account indicates dealing with the complexity of optimization and an increase in variables and the calculation cost, which also presents a challenge in OPU optimization.…”