Research on the impact of important parameters of component cooling system on design

Abstract

There is a significant gap between the component cooling system (CCS) of the HPR1000 nuclear island cold chain system and the actual operating parameters, resulting in insufficient utilization of the cooling source and poor economic performance. Due to the high complexity of CCS design and the difficulty of design evaluation, multi-objective optimization research is necessary to identify parameters that significantly impact the design results of the CCS. To this end, this paper constructs a mathematical model and evaluation program for the CCS, utilizing an improved genetic algorithm suitable for solving constrained complex multi-objective optimization problems. The design temperature of the CCS and several large design flow rates are selected as optimization variables, while weight, volume, investment cost, and energy consumption are set as optimization objectives. The aim is to use optimization design methods to determine the impact of important parameters of the CCS on its design. The results indicate that, under the premise of ensuring the safety of nuclear power plants, the optimization algorithm can effectively identify the optimal combinations of optimization variables, leading to relative optimal solutions for the four objectives: a maximum reduction of 18.1 % in weight, 21.3 % in volume, 11.5 % in investment cost, and 28.7 % in energy consumption. Furthermore, the study finds that the design temperature of the CCS is a crucial parameter affecting its design results, and that appropriately reducing the design temperature is a key direction for optimizing the CCS design.