Multi-Objective Design Optimization for A Conceptual Passive Containment Cooling System with Closed-Loop Configuration

In this research, a set of models were set up and corresponding codes（PCCS-CL） were developed for the prediction of the performance of proposed conceptual passive containment cooling system（PCCS） based on one-dimensional homogeneous two phase flow model. An improved non-dominated genetic algorithm（INGA） was developed with the sorting algorithm, improved crowding-distance and optimum retention strategy. The multi-objective design optimization for the proposed PCCS was conducted by using developed codes of INGA. The sensitive study on key parameters show that the diameter of heat transfer tube both for in-containment heat exchanger and ex-containment heat exchanger plays a critical role for the heat removal capacity of PCCS. For the range of parameters in this paper, either reducing the inside diameter or increasing the length of heat transfer tube is helpful for improving the heat removal capacity of the system. The optimized scheme given in this study might provide references for the engineering design of PCCS.