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Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system

Jun Li1,2, Hal Gurgenci3, Zhiqiang Guan3

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This study optimizes turbine rotor performance using advanced algorithms. Quantum harmonic oscillator perturbation significantly impacts convergence and design, revealing complex multi-modal optimization challenges.

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Area of Science:

  • Mechanical Engineering
  • Computational Dynamics

Background:

  • Accurate modeling of turbine rotor performance, particularly unbalance response and critical speed, is crucial.
  • Support system dynamic fidelity significantly influences the accuracy of these performance predictions.

Purpose of the Study:

  • To explore the effectiveness of multi-scale quantum harmonic oscillator (MQHO) and genetic algorithms (GA) in capturing turbine rotor performance.
  • To investigate the ability of different algorithm models to represent non-dominated solutions in objective function interactions.
  • To optimize rotor geometry using a multi-objective framework with high-fidelity models.

Main Methods:

  • Implementation of lobal optimization methods, specifically MQHO and GA.
  • Deployment of support system models within a multi-objective optimization framework.
  • Integration of a rotor finite element model with parameters to guide geometric search over harmonic response modes.

Main Results:

  • MQHO and hybrid GA effectively capture high-fidelity model behavior with reduced computational cost.
  • MQHO perturbation emerged as a dominant factor influencing convergence speed and repetition retention.
  • Control parameters and convergence scale were critical for optimization success.
  • Several design candidates closely approximated non-dominant frontiers, indicating conflicting objectives and multi-modal characteristics.

Conclusions:

  • MQHO algorithms offer a computationally efficient approach for turbine rotor performance optimization.
  • The study highlights the complex, multi-modal nature of rotor design optimization with conflicting objectives.
  • Understanding support system dynamics is key to achieving accurate turbine rotor performance predictions.