Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties
Robust optimization design (ROD) is playing an increasingly significant role in aerodynamic shape optimization and aircraft design. However, an efficient ROD framework that couples uncertainty quantification (UQ) and a powerful optimization algorithm for three-dimensional configurations is lacking....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/10/831 |
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| author | Yuhang Ma Jiecheng Du Tihao Yang Yayun Shi Libo Wang Wei Wang |
| author_facet | Yuhang Ma Jiecheng Du Tihao Yang Yayun Shi Libo Wang Wei Wang |
| author_sort | Yuhang Ma |
| collection | DOAJ |
| description | Robust optimization design (ROD) is playing an increasingly significant role in aerodynamic shape optimization and aircraft design. However, an efficient ROD framework that couples uncertainty quantification (UQ) and a powerful optimization algorithm for three-dimensional configurations is lacking. In addition, it is very important to reveal the maintenance mechanism of aerodynamic robustness from the design viewpoint. This paper first combines gradient-based optimization using the discrete adjoint-based approach with the polynomial chaos expansion (PCE) method to establish the ROD framework. A flying-wing configuration is optimized using deterministic optimization and ROD methods, respectively. The uncertainty parameters are Mach and the angle of attack. The ROD framework with the mean as an objective achieves better robustness with a lower mean (6.7% reduction) and standard derivation (Std, 18.92% reduction) compared to deterministic results. Moreover, we only sacrifice a minor amount of the aerodynamic performance (an increment of 0.64 counts in the drag coefficient). In comparison, the ROD with Std as an objective obtains a very different result, achieving the lowest Std and largest mean The far-field drag decomposition method is applied to compute the statistical moment variation of drag components and reveal how the ROD framework adjusts the drag component to realize better aerodynamic robustness. The ROD with the mean as the objective decreases the statistical moment of each drag component to improve aerodynamic robustness. In contrast, the ROD with Std as an objective reduces Std significantly by maintaining the inverse correlation relationship between the induced drag and viscous drag with an uncertainty parameter, respectively. The established ROD framework can be applied to future engineering applications that consider uncertainties. The unveiled mechanism for maintaining aerodynamic robustness will help designers understand ROD results more deeply, enabling them to reasonably construct ROD optimization problems. |
| first_indexed | 2024-03-10T21:31:33Z |
| format | Article |
| id | doaj.art-ab16e391f9614a609dac6deeb5bba742 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T21:31:33Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-ab16e391f9614a609dac6deeb5bba7422023-11-19T15:16:46ZengMDPI AGAerospace2226-43102023-09-01101083110.3390/aerospace10100831Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating UncertaintiesYuhang Ma0Jiecheng Du1Tihao Yang2Yayun Shi3Libo Wang4Wei Wang5School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaAVIC The First Aircraft Institute, Xi’an 710089, ChinaAVIC The First Aircraft Institute, Xi’an 710089, ChinaRobust optimization design (ROD) is playing an increasingly significant role in aerodynamic shape optimization and aircraft design. However, an efficient ROD framework that couples uncertainty quantification (UQ) and a powerful optimization algorithm for three-dimensional configurations is lacking. In addition, it is very important to reveal the maintenance mechanism of aerodynamic robustness from the design viewpoint. This paper first combines gradient-based optimization using the discrete adjoint-based approach with the polynomial chaos expansion (PCE) method to establish the ROD framework. A flying-wing configuration is optimized using deterministic optimization and ROD methods, respectively. The uncertainty parameters are Mach and the angle of attack. The ROD framework with the mean as an objective achieves better robustness with a lower mean (6.7% reduction) and standard derivation (Std, 18.92% reduction) compared to deterministic results. Moreover, we only sacrifice a minor amount of the aerodynamic performance (an increment of 0.64 counts in the drag coefficient). In comparison, the ROD with Std as an objective obtains a very different result, achieving the lowest Std and largest mean The far-field drag decomposition method is applied to compute the statistical moment variation of drag components and reveal how the ROD framework adjusts the drag component to realize better aerodynamic robustness. The ROD with the mean as the objective decreases the statistical moment of each drag component to improve aerodynamic robustness. In contrast, the ROD with Std as an objective reduces Std significantly by maintaining the inverse correlation relationship between the induced drag and viscous drag with an uncertainty parameter, respectively. The established ROD framework can be applied to future engineering applications that consider uncertainties. The unveiled mechanism for maintaining aerodynamic robustness will help designers understand ROD results more deeply, enabling them to reasonably construct ROD optimization problems.https://www.mdpi.com/2226-4310/10/10/831discrete adjoint methodpolynomial chaos expansionuncertainty quantificationrobust optimization designfar-field drag decompositionglobal sensitivity analysis |
| spellingShingle | Yuhang Ma Jiecheng Du Tihao Yang Yayun Shi Libo Wang Wei Wang Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties Aerospace discrete adjoint method polynomial chaos expansion uncertainty quantification robust optimization design far-field drag decomposition global sensitivity analysis |
| title | Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties |
| title_full | Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties |
| title_fullStr | Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties |
| title_full_unstemmed | Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties |
| title_short | Aerodynamic Robust Design Research Using Adjoint-Based Optimization under Operating Uncertainties |
| title_sort | aerodynamic robust design research using adjoint based optimization under operating uncertainties |
| topic | discrete adjoint method polynomial chaos expansion uncertainty quantification robust optimization design far-field drag decomposition global sensitivity analysis |
| url | https://www.mdpi.com/2226-4310/10/10/831 |
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