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

The importance of support system dynamic fidelity in capturing turbine rotor performance in unbalance response and critical speed is considered. Lobal optimization methods based on multi-scale quantum harmonic oscillator algorithm and genetic algorithm are used, and the extent to which the non-domin...

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Vydané v:Scientific reports Ročník 15; číslo 1; s. 7534 - 16
Hlavní autori: Li, Jun, Gurgenci, Hal, Guan, Zhiqiang, Wang, Jiaxin, Chen, Junwen, Wang, Chen, Huang, Zhenyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 04.03.2025
Nature Publishing Group
Nature Portfolio
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639/166/988
Algorithms
Convergence
Design
Fidelity
Genetic algorithms
Humanities and Social Sciences
Multi-objective optimization
Multi-scale convergence
multidisciplinary
Objective function
Optimization
Oscillators
Science
Science (multidisciplinary)
Turbine shaft system
Multi-scale convergence
Multi-objective optimization
Turbine shaft system
ISSN:2045-2322, 2045-2322
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Shrnutí:The importance of support system dynamic fidelity in capturing turbine rotor performance in unbalance response and critical speed is considered. Lobal optimization methods based on multi-scale quantum harmonic oscillator algorithm and genetic algorithm are used, and the extent to which the non-dominated solution of objective function interactions is appropriately captured by the different algorithm models is explored. The support system models are deployed within a multi-objective optimization framework. This framework pairs a rotor finite element model with parameters to guide the search for an optimal geometry over harmonic response modes. We demonstrate the use of the quantum harmonic oscillator algorithms and hybrid genetic algorithm to capture the behavior of the high-fidelity model over the design space with reduced computational needs. Results of the optimization show quantum harmonic oscillator perturbation to be a dominant factor, with different design implications for convergence speed and repetition retention. Control parameters and convergence scale were also critical. Importantly, a number of design candidates were encountered during the optimization that performed very closely to the non-dominant frontiers, highlighting the conflicting objective functions and multi-modal nature of the problem.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-92070-0