Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92070-0 |
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| author | Jun Li Hal Gurgenci Zhiqiang Guan Jiaxin Wang Junwen Chen Chen Wang Zhenyu Huang |
| author_facet | Jun Li Hal Gurgenci Zhiqiang Guan Jiaxin Wang Junwen Chen Chen Wang Zhenyu Huang |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-adf4f0e46b4d4e54b86ca77389b0f13d |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-adf4f0e46b4d4e54b86ca77389b0f13d2025-08-20T01:57:51ZengNature PortfolioScientific Reports2045-23222025-03-0115111610.1038/s41598-025-92070-0Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimizationJun Li0Hal Gurgenci1Zhiqiang Guan2Jiaxin Wang3Junwen Chen4Chen Wang5Zhenyu Huang6School of Automotive Intelligent Manufacturing, Hubei University of Automotive TechnologySchool of Mechanical and Mining Engineering, University of QueenslandSchool of Mechanical and Mining Engineering, University of QueenslandSchool of Automotive Intelligent Manufacturing, Hubei University of Automotive TechnologySchool of Automotive Intelligent Manufacturing, Hubei University of Automotive TechnologySchool of Automotive Intelligent Manufacturing, Hubei University of Automotive TechnologySchool of Automotive Intelligent Manufacturing, Hubei University of Automotive TechnologyAbstract 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.https://doi.org/10.1038/s41598-025-92070-0Multi-objective optimizationTurbine shaft systemMulti-scale convergence |
| spellingShingle | Jun Li Hal Gurgenci Zhiqiang Guan Jiaxin Wang Junwen Chen Chen Wang Zhenyu Huang Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization Scientific Reports Multi-objective optimization Turbine shaft system Multi-scale convergence |
| title | Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization |
| title_full | Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization |
| title_fullStr | Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization |
| title_full_unstemmed | Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization |
| title_short | Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization |
| title_sort | multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization |
| topic | Multi-objective optimization Turbine shaft system Multi-scale convergence |
| url | https://doi.org/10.1038/s41598-025-92070-0 |
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