Electric heating pressurizer in nuclear power plant plays a key role in pressure control and protection of primary loop. Small size pressurizer fulfilling the performance requirements has been receiving an increasing amount of attention because of relatively low cost and easy installation. However, the size of pressurizer is sensitive to the thermal-hydraulic and structure parameters and it can only be determined in the thermal design of pressurizer.

In this work, a thermal-hydraulic model of pressurizer was established. Based on this model, the pressurizer inner diameter, primary loop pressure, reactor outlet coolant temperature and pressurizer spray coefficient were taken as design variables, and the weight and volume of pressurizer were taken as optimization objectives. Sensitivity analysis between the design variables and steady-state minimum water volume, steady-state minimum steam volume and steady-state power change volume was implemented respectively to investigate the influence of the variables on the pressurizer thermal hydraulic performance. Sensitivity analysis between the design variables and optimization objectives was also implemented to study the relation between them. The results show that primary loop pressure and reactor outlet coolant temperature obviously effect the fluctuation volume when a positive/negative fluctuation or a power change happens. The primary loop pressure has a negative correlation with the fluctuation volume, while the correlation between the reactor outlet coolant temperature and fluctuation volume is positive. The pressurizer spray coefficient only works in positive fluctuation because there is no need to spray in other conditions. As a structure parameter, the pressurizer inner diameter has little effect on the fluctuation, but it has obvious influence on the weight and volume of pressurizer just as other design variables. Finally, the multi-objective optimization of pressurizer aiming at minimizing the weight and volume was performed by utilizing a hybrid non-dominated sorting genetic algorithm. The optimization result is illustrated in a 2D graph. It shows that there exists a feasible region that the weight and volume of the pressurizer is as much as 10 percentage smaller compared with the original one, which indicates the validity of multi-objective optimization of pressurizer.

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