Current research on performance improvement of negative pressure adsorption system mainly focuses on the negative pressure cavity but does not involve the influence of the blade shape on adsorption performance. Furthermore, the interactions among various variables and the conflicts among multiple objectives make the optimization process more challengeable. This research proposed an optimization method that fully considers the influence from both blade shape and negative pressure cavity, with aim to increase adsorption force and decrease power consumption in a case study. We selected four parameters of the blade shape and a gap height as optimization variables using sensitivity analysis method, utilized Latin hypercube sampling (LHS) and computational fluid dynamics (CFD) to generate and compute 80 sets of samples, built up the surrogate model between optimization variables and objectives by integrating particle swarm optimization (PSO) algorithm with back propagation (BP) neural network, conducted multi-objective optimization using non-dominated sorting genetic algorithm III (NSGA-III), and finally identified optimal solution using entropy weight TOPSIS method. Compared with the original design, the optimal design achieved an increase of adsorption force by 41.81% and a decrease of power consumption by 17.97% simultaneously. This research presented an effective optimization method to conduct multi-variable and multi-objective performance improvement for the negative pressure adsorption systems.
MENG et al. (Thu,) studied this question.