Research on the Dynamic Characteristics Analysis and Power Control Method of Heat Pipe Reactors

Abstract

A heat pipe reactor (HPR) is a kind of modular small reactor with broad application prospects, and its dynamic characteristics and nuclear power control are essential to the safe and stable operation of nuclear power plants. Taking the MegaPower HPR as an example, the dynamic characteristics of the HPR are analyzed, and its power control method is designed in this paper. Based on the lumped parameter idea, the equivalent processing of the structure of the HPR core is carried out, and the main parameters of the heat pipe heat exchanger are designed at first. A lightweight dynamic model of the HPR is established using a thermal resistance network, and the accuracy of the model is verified using the solution of the model under the steady-state full power condition. Then, the dynamic characteristics of the HPR without a controller are analyzed respectively with the disturbance reactivity and mass flow rate, indicating strong self-stability and self-regulation of the HPR. Finally, a reinforcement learning (RL) controller based on the twin delayed deep deterministic policy gradient (TD3) algorithm is designed for the HPR power control, and it is adjusted through appropriately setting states, network structures, reward functions, etc. To verify the performance of the controller, a step response simulation ranging from 100%FP to 90%FP, a compound conditions simulation, and a large load change simulation are carried out, respectively. The results show that the RL controller can find the optimal control strategy through training. Meanwhile, it significantly improves the dynamic and steady-state performance of nuclear power compared with uncontrolled case and PID controller case, and it has the ability of power control under all operating conditions.