Multi-objective optimization integrating weighted average surrogate model and NSGA-II intelligent algorithm applied to a self-excited oscillation mixer used in mixed flow descaling

Abstract

This study proposes a multi-objective optimization method based on a hybrid of computational fluid dynamics (CFD) and experimental techniques, which integrates a weighted average surrogate model and the NSGA-II intelligent algorithm. To validate the feasibility of this method, structural optimization for the self-excited oscillation mixer (SEOM) was conducted. The optimization objectives aimed to reduce potential abrasives accumulation in the mixed-flow cavity and minimize erosion on the elbow of the slurry outlet caused by mixed slurry, thereby enhancing the service life and rust removal efficiency of the mixed flow rotary jet descaling (MFD). A comparison between the optimized model and original model revealed a 2.51% increase in negative pressure within the cavity of the optimized model, as well as a 10.36% decrease in velocity at the outlet of mixed slurry based on simulation results. Experimental methods measured both mass flow rate of inhaled abrasive particles and impact force of mixed slurry outlet. The experimental results demonstrated that compared to its original counterpart, the optimized model exhibited superior performance with a 22.81% increase in abrasive particle flow rate and an 11.34% reduction in mixed slurry impact force indirectly, verifying better simulation effectiveness for SEOM with optimized structure. This approach also enhanced efficiency and surface quality of MFD processing strip steel, providing guidance for structural optimization of similar SEOMs used in mixed flow descaling.