Atomization performance optimization of series dual-chamber self-excited oscillation nozzle using the entropy weight method combined with gray theory

In this study, a series dual-chamber self-excited oscillation nozzle (SDSON) for atomization was developed for photodecomposition of oily wastewater. In order to address the computational complexity associated with optimizing this nozzle, a surrogate model that integrates computational fluid dynamics simulation is proposed. By employing a multi-objective optimization algorithm that combines Genetic Algorithm and Non-dominated Sorting Genetic Algorithm II, significant improvements in atomization performance have been achieved. The influencing factors of atomization and their interactions on the nozzle's atomization performance have been analyzed. The entropy weight method was employed in conjunction with gray theory to rank the optimal solutions based on weighted correlation evaluation, resulting in the determination of the most favorable design solutions. The optimized design exhibited significant enhancements in turbulence kinetic energy and gas volume fraction at the nozzle outlet. Atomization experiments confirmed that the optimized SDSON generated smaller and more uniformly sized droplets under identical inlet pressure conditions, thereby greatly improving atomization performance.