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

Chuanzhi Sun1, Qing Lu2, Yinchu Wang1

  • 1Center of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China.

The Review of Scientific Instruments
|December 3, 2022
PubMed
Summary
This summary is machine-generated.

An improved genetic algorithm (GA) significantly reduces high-pressure compressor imbalance by optimizing rotor blade arrangement and assembly phase. This method achieves a 99.46% simulation reduction, enhancing assembly quality.

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Area of Science:

  • Mechanical Engineering
  • Computational Engineering
  • Aerospace Engineering

Background:

  • High-pressure compressors are critical in many industries, but low quality rates in one-time assembly pose significant challenges.
  • Rotor imbalance after assembly directly impacts compressor performance and operational lifespan.
  • Optimizing assembly parameters is crucial for achieving high-quality, reliable compressor systems.

Purpose of the Study:

  • To develop and validate an improved genetic algorithm (GA) for optimizing the post-assembly imbalance of high-pressure compressor rotors.
  • To enhance the quality rate of one-time compressor assembly through intelligent optimization techniques.
  • To reduce post-assembly imbalance by adjusting rotor blade arrangement and inter-rotor assembly phase.

Main Methods:

  • An improved genetic algorithm (GA) was employed, incorporating a four-sector staggered distribution for initial population generation and an elite retention strategy.
  • Adaptive adjustment of crossover and mutation probabilities based on fitness function values was implemented.
  • A threshold termination condition was added to ensure rapid convergence and efficient search for optimal solutions.

Main Results:

  • Simulations demonstrated a 99.46% reduction in imbalance using the improved GA, outperforming traditional GA methods.
  • Experimental validation showed significant reductions in imbalance on two correction surfaces, reaching 640 and 760 g·mm.
  • The improved GA achieved 86.7% and 87.1% better results compared to zero-degree assembly methods.

Conclusions:

  • The improved genetic algorithm effectively minimizes post-assembly imbalance in high-pressure compressors.
  • This optimization approach leads to substantial improvements in assembly quality and efficiency.
  • The method offers a robust solution for enhancing the reliability and performance of high-pressure compressor systems.