Multi-objective optimization of the geometric parameters of a pressure-swirl nozzle

Abstract

ABSTRACT The geometric parameters of the pressure-swirl nozzle demonstrate a significant effect on the atomization performance such as the spray cone angle and mass flow rate. However, these objectives are incompatible, and obtaining a nozzle geometric parameter that optimizes all the atomization performances is difficult. The effectiveness of the nozzle optimization process is typically low, resulting in high costs. To this purpose, an enhanced CBSO-Kriging surrogate model was developed to solve this expensive black box problem, and the spray cone angle and mass flow rate were optimized using a multi-objective optimization algorithm. Firstly, the optimal Latin hypercube sampling method was used to generate 65 sets of sample points, and the corresponding response values were calculated using CFD simulation. The established surrogate model of atomization performance can predict the atomization characteristics of the nozzle with arbitrary shape. The correlation between the key geometric parameters and the spray cone angle and the mass flow rate was determined on this basis. Then, to find the optimal geometric parameters for the nozzle, the NSGA-II algorithm was used. In comparison to the initial model, the optimization results show that the spray cone angle and mass flow rate increased by 4.26% and 37.74%, respectively.