Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm
The current challenges encountered in Surrogate-Based Optimization (SBO) primarily stem from the substantial number of function calls essential for accurate evaluations. A promising approach to alleviate this problem is to leverage Gaussian Process Regression (GPR) models integrated with Automatic K...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2456500 |
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| author | Youtao Xue Yuxin Yang Shaobo Yao Wenwen Zhao Lihua Chen |
| author_facet | Youtao Xue Yuxin Yang Shaobo Yao Wenwen Zhao Lihua Chen |
| author_sort | Youtao Xue |
| collection | DOAJ |
| description | The current challenges encountered in Surrogate-Based Optimization (SBO) primarily stem from the substantial number of function calls essential for accurate evaluations. A promising approach to alleviate this problem is to leverage Gaussian Process Regression (GPR) models integrated with Automatic Kernel Construction (AKC) algorithms, known for their superior fitting precision with reduced sample sizes. This study introduces an SBO framework tailored for Gaussian process regression models, featuring an automatic kernel construction algorithm coupled with a beam search strategy (AKC-GPR). The primary aim of this framework is to explore the efficiency enhancements achievable through AKC-GPR. By using the benchmark two-dimensional Rosenbrock function for comprehensive evaluation, the results show that when optimizing this function, the AKC-GPR model reduces the number of actual function calls by 45%, and the optimization results are closer to the true value. In addition, to further demonstrate the effectiveness of the framework, a benchmark optimization of aerodynamic shape for drag minimization of a RAE2822 wing under transonic viscous flow, proposed by the American Institute of Aeronautics and Astronautics (AIAA) Aerodynamic Design Optimization Discussion Group (ADODG), was conducted. Compared to the conventional GPR-based SBO approach, our AKC-GPR framework significantly reduces the number of required computational fluid dynamics (CFD) simulations by 27.7% while improving drag reduction by 2.83%. Qualitative and approximate comparative analysis of the results with other research groups on the same benchmark case validates the robustness and effectiveness of our proposed optimization framework. |
| format | Article |
| id | doaj-art-40c5d57baa6045ffa83e669f9fcf8de6 |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-40c5d57baa6045ffa83e669f9fcf8de62025-01-30T09:43:45ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2025-12-0119110.1080/19942060.2025.2456500Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithmYoutao Xue0Yuxin Yang1Shaobo Yao2Wenwen Zhao3Lihua Chen4School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People’s Republic of ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People’s Republic of ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People’s Republic of ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People’s Republic of ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou, People’s Republic of ChinaThe current challenges encountered in Surrogate-Based Optimization (SBO) primarily stem from the substantial number of function calls essential for accurate evaluations. A promising approach to alleviate this problem is to leverage Gaussian Process Regression (GPR) models integrated with Automatic Kernel Construction (AKC) algorithms, known for their superior fitting precision with reduced sample sizes. This study introduces an SBO framework tailored for Gaussian process regression models, featuring an automatic kernel construction algorithm coupled with a beam search strategy (AKC-GPR). The primary aim of this framework is to explore the efficiency enhancements achievable through AKC-GPR. By using the benchmark two-dimensional Rosenbrock function for comprehensive evaluation, the results show that when optimizing this function, the AKC-GPR model reduces the number of actual function calls by 45%, and the optimization results are closer to the true value. In addition, to further demonstrate the effectiveness of the framework, a benchmark optimization of aerodynamic shape for drag minimization of a RAE2822 wing under transonic viscous flow, proposed by the American Institute of Aeronautics and Astronautics (AIAA) Aerodynamic Design Optimization Discussion Group (ADODG), was conducted. Compared to the conventional GPR-based SBO approach, our AKC-GPR framework significantly reduces the number of required computational fluid dynamics (CFD) simulations by 27.7% while improving drag reduction by 2.83%. Qualitative and approximate comparative analysis of the results with other research groups on the same benchmark case validates the robustness and effectiveness of our proposed optimization framework.https://www.tandfonline.com/doi/10.1080/19942060.2025.2456500Aerodynamic shape optimizationsurrogate-based optimizationGaussian process regressionautomatic kernel construction |
| spellingShingle | Youtao Xue Yuxin Yang Shaobo Yao Wenwen Zhao Lihua Chen Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm Engineering Applications of Computational Fluid Mechanics Aerodynamic shape optimization surrogate-based optimization Gaussian process regression automatic kernel construction |
| title | Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm |
| title_full | Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm |
| title_fullStr | Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm |
| title_full_unstemmed | Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm |
| title_short | Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm |
| title_sort | optimizing aerodynamic shape of benchmark problems using an improved gaussian process regression algorithm |
| topic | Aerodynamic shape optimization surrogate-based optimization Gaussian process regression automatic kernel construction |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2025.2456500 |
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