Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces
In aerodynamic shape optimization, geometric parameterized variables have a significant impact on the flow field, thereby influencing both the effectiveness and efficiency of the optimization process. This paper utilizes flow field data from computational fluid dynamics (CFD) to develop a data-drive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/12/984 |
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| author | Xiaoyu Xu Hongbo Chen Chenliang Zhang Yanhui Duan Guangxue Wang |
| author_facet | Xiaoyu Xu Hongbo Chen Chenliang Zhang Yanhui Duan Guangxue Wang |
| author_sort | Xiaoyu Xu |
| collection | DOAJ |
| description | In aerodynamic shape optimization, geometric parameterized variables have a significant impact on the flow field, thereby influencing both the effectiveness and efficiency of the optimization process. This paper utilizes flow field data from computational fluid dynamics (CFD) to develop a data-driven approach for analyzing the influence of geometric parameterized variables on the objective function and flow field across various design spaces. A data-driven method, namely a sensitivity analysis based on the Kriging model, is proposed along with three design space variation schemes (scaling, translation, and their combination) to evaluate the influence of geometric parameters on the objective function under varying design spaces. Furthermore, the study investigates the effects of these design space variation schemes on the sensitivity results using two test functions and a wave drag reduction case. The results of the wave drag reduction case are further analyzed in relation to flow conditions, including the Mach number and shock-wave strength. The findings indicate that design space variations alter the relationship between geometric parameters and the flow field, particularly affecting the shock-wave position and strength, as reflected by the sensitivity indices of the variables. Additionally, the sensitivity results show a strong dependence on the Mach number under varying design space configurations. |
| format | Article |
| id | doaj-art-e865826855ca40b9a616f2fe00f2c2b5 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-e865826855ca40b9a616f2fe00f2c2b52024-12-27T14:02:27ZengMDPI AGAerospace2226-43102024-11-01111298410.3390/aerospace11120984Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design SpacesXiaoyu Xu0Hongbo Chen1Chenliang Zhang2Yanhui Duan3Guangxue Wang4School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510000, ChinaSchool of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510000, ChinaSchool of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510000, ChinaSchool of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510000, ChinaSchool of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 515100, ChinaIn aerodynamic shape optimization, geometric parameterized variables have a significant impact on the flow field, thereby influencing both the effectiveness and efficiency of the optimization process. This paper utilizes flow field data from computational fluid dynamics (CFD) to develop a data-driven approach for analyzing the influence of geometric parameterized variables on the objective function and flow field across various design spaces. A data-driven method, namely a sensitivity analysis based on the Kriging model, is proposed along with three design space variation schemes (scaling, translation, and their combination) to evaluate the influence of geometric parameters on the objective function under varying design spaces. Furthermore, the study investigates the effects of these design space variation schemes on the sensitivity results using two test functions and a wave drag reduction case. The results of the wave drag reduction case are further analyzed in relation to flow conditions, including the Mach number and shock-wave strength. The findings indicate that design space variations alter the relationship between geometric parameters and the flow field, particularly affecting the shock-wave position and strength, as reflected by the sensitivity indices of the variables. Additionally, the sensitivity results show a strong dependence on the Mach number under varying design space configurations.https://www.mdpi.com/2226-4310/11/12/984data-drivensensitivity analysisgeometric parameterized variablesdesign space variationdrag reduction |
| spellingShingle | Xiaoyu Xu Hongbo Chen Chenliang Zhang Yanhui Duan Guangxue Wang Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces Aerospace data-driven sensitivity analysis geometric parameterized variables design space variation drag reduction |
| title | Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces |
| title_full | Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces |
| title_fullStr | Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces |
| title_full_unstemmed | Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces |
| title_short | Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces |
| title_sort | data driven sensitivity analysis of the influence of geometric parameterized variables on flow fields under different design spaces |
| topic | data-driven sensitivity analysis geometric parameterized variables design space variation drag reduction |
| url | https://www.mdpi.com/2226-4310/11/12/984 |
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