Design optimization for pressurized water reactor using improved quantum fish swarm algorithm and intuitionistic linguistic decision-making

Nuclear power has garnered significant attention as a clean energy resource in expediting carbon peaking and achieving carbon neutrality. It contributes to sustainable economic growth while addressing national energy security concerns. As one of the most crucial components, pressurized water reactors require improvements in several key aspects, including strengthening the natural circulation capacity, increasing the reactor power, and reducing the reactor volume. However, the volume of pressurized water reactors is usually positively correlated with their power. Increasing the reactor power results in an expansion of its volume, which poses challenges for the reactor layout and also requires additional investment. Hence, in order to reduce the reactor volume while meeting its performance requirements, this study introduces a novel design optimization approach utilizing an improved quantum fish swarm algorithm and intuitionistic linguistic decision-making. Firstly, a nonlinear mathematical model for volume optimization of the steam generator (SG) and pressurizer (PR) is constructed. Then, the improved quantum fish swarm algorithm is proposed to obtain volume optimization alternatives for the SG and PR. Quantum computation strategies are introduced to enhance the global optimization ability and population diversity. Additionally, the vision range and step size of the quantum fish swarm are adjusted adaptively to improve the optimization precision. To further accelerate the algorithm convergence, the prospect elimination strategy is built. Last but not least, the intuitionistic linguistic decision-making is presented to gain the optimal design alternative by taking four characteristics into account, namely, the fuzziness and hesitancy of evaluation information, the consensus reaching process, the bounded rationality of decision-making behaviors, and the complementarity, independence and redundancy among nuclear evaluation criteria. A case study on the SG and PR design optimization is implemented to demonstrate the reliability and reasonability of the proposed approach.