Multi-Objective Cooperative Optimization of Key Structural Parameters of Pressure Swirl Nozzles Under Microgravity

This study presents a surrogate-assisted multi-objective numerical framework to analyze and optimize the coupled effects of geometric parameters on the spray cone angle and discharge coefficient of a pressure swirl nozzle under microgravity conditions. Three-dimensional CFD simulations combined with an orthogonal experimental design were used to construct a structured dataset, on which parameter sensitivity and objective inconsistency were diagnosed using the Analytic Hierarchy Process. Back-propagation neural network surrogate models were then developed and coupled with the NSGA-II algorithm to explore the continuous design space and identify Pareto-optimal solutions. CFD re-evaluations show that, under identical operating conditions, the Pareto-based optimized configuration increases the spray cone angle from 64.71° to 69.14° while improving the discharge coefficient from 0.2736 to 0.3903 relative to the baseline nozzle. The results demonstrate that explicitly accounting for the trade-off between spray spreading and flow capacity enables a more balanced nozzle design than conventional weighted optimization approaches, providing a quantitative reference for microgravity-relevant injector design.