Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings
Abstract Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aero...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82573-7 |
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| author | Qing Guo Xiaoqiang Li Zhijie Zhou Dexiao Ma Yuzhuo Wang |
| author_facet | Qing Guo Xiaoqiang Li Zhijie Zhou Dexiao Ma Yuzhuo Wang |
| author_sort | Qing Guo |
| collection | DOAJ |
| description | Abstract Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity. Therefore, a novel neural network (NN)-based method for aeroelastic system identification is proposed. The proposed NN-based approach constructs an NN framework of high flexibility wings flutter models with different materials and sizes, which can effectively predict the flutter velocity of flexible wings. The accuracy of the method is demonstrated by comparing with the simulation results. |
| format | Article |
| id | doaj-art-1b381f759ad041428a1c5a90fa9d7c9a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1b381f759ad041428a1c5a90fa9d7c9a2025-01-05T12:23:28ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-82573-7Neural network-based aeroelastic system identification for predicting flutter of high flexibility wingsQing Guo0Xiaoqiang Li1Zhijie Zhou2Dexiao Ma3Yuzhuo Wang4School of Aeronautics, Northwestern Polytechnical UniversitySchool of Aeronautics, Northwestern Polytechnical UniversitySchool of Aeronautics, Northwestern Polytechnical UniversitySchool of Aeronautics, Northwestern Polytechnical UniversitySchool of Aeronautics, Northwestern Polytechnical UniversityAbstract Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity. Therefore, a novel neural network (NN)-based method for aeroelastic system identification is proposed. The proposed NN-based approach constructs an NN framework of high flexibility wings flutter models with different materials and sizes, which can effectively predict the flutter velocity of flexible wings. The accuracy of the method is demonstrated by comparing with the simulation results.https://doi.org/10.1038/s41598-024-82573-7FlutterHigh Flexibility wingsNeural networkAeroelasticity |
| spellingShingle | Qing Guo Xiaoqiang Li Zhijie Zhou Dexiao Ma Yuzhuo Wang Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings Scientific Reports Flutter High Flexibility wings Neural network Aeroelasticity |
| title | Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings |
| title_full | Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings |
| title_fullStr | Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings |
| title_full_unstemmed | Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings |
| title_short | Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings |
| title_sort | neural network based aeroelastic system identification for predicting flutter of high flexibility wings |
| topic | Flutter High Flexibility wings Neural network Aeroelasticity |
| url | https://doi.org/10.1038/s41598-024-82573-7 |
| work_keys_str_mv | AT qingguo neuralnetworkbasedaeroelasticsystemidentificationforpredictingflutterofhighflexibilitywings AT xiaoqiangli neuralnetworkbasedaeroelasticsystemidentificationforpredictingflutterofhighflexibilitywings AT zhijiezhou neuralnetworkbasedaeroelasticsystemidentificationforpredictingflutterofhighflexibilitywings AT dexiaoma neuralnetworkbasedaeroelasticsystemidentificationforpredictingflutterofhighflexibilitywings AT yuzhuowang neuralnetworkbasedaeroelasticsystemidentificationforpredictingflutterofhighflexibilitywings |