Strengthened elitist genetic algorithm for aeroengine blade arrangement optimisation

Abstract

Variations in mass moment of aeroengine blades can cause different residual unbalances, leading to exceeding vibration limit values. For this reason, an accurate and rapid selection of a suitable blade arrangement is essential for improving assembly quality and efficiency. This work innovatively applies the strengthened elitist genetic algorithm (SEGA) to the optimization of blade arrangement for aeroengine. This work investigates the effects of population size (Ps: 100–500), population crossover probability (Pc: 0.6–0.9), and population mutation probability (Pm: 0.6–0.9) on the convergence speed and accuracy of the algorithm, respectively. The obtained results indicate that the optimal parameters of the algorithm are 300 for Ps, 0.7 for Pc, and 0.9 for Pm, which can quickly search for high-precision solution. Compared to the elitist genetic algorithm (EGA), the accuracy value of SEGA is improved about 82%. In addition, this work conducted simulated vibration platform to verify SEGA, and the obtained results show that the vibration values are reduced by 4.19%, 10.19%, and 2.99% at stable speeds of 1000, 1500, and 2000 rpm, respectively, compared to that of group sorting. This work employs a novel SEGA that can effectively improve the accuracy value and reduce vibration values compared to EGA and group sorting, respectively. The above may reduce the residual unbalances of the aeroengine, improve the quality of assembly, and provide a new idea for the assembly of blades.