A two-variable control and optimization method for imbalance of high pressure compressor based on improved genetic algorithm

To solve the problem of low quality rate for one-time assembly of high-pressure compressors, an improved genetic algorithm (GA) is used to adjust and optimize the imbalance after assembly. This paper takes the post-assembly imbalance of a multi-stage rotor of a high-pressure compressor as the objective function, to reduce the post-assembly imbalance by adjusting the arrangement order of rotor blades and the assembly phase between rotors. We used a four-sector staggered distribution method to generate high-quality initial populations and added an elite retention strategy. The crossover and mutation probabilities are adaptively adjusted according to the fitness function values. The threshold termination condition is added to make the algorithm converge quickly so as to achieve fast, stable, and efficient search. The simulation results show that the imbalance is reduced by 99.46% by using the improved genetic algorithm, which is better than the traditional GA. The experimental results show that the imbalance of the two correction surfaces can be reduced to 640 and 760 g.mm, respectively, which is 86.7% and 87.1% better than the zero-degree assembly.