End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization
Aerodynamic shape design optimization faces challenges due to the computational demands and the vast design space, limiting its practicality and scalability. While progress has been made in subsonic and transonic regimes, the real-time optimization for supersonic conditions remains unexplored. To br...
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MDPI AG
2025-04-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/5/389 |
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| author | Diogo Pereira Frederico Afonso Fernando Lau |
| author_facet | Diogo Pereira Frederico Afonso Fernando Lau |
| author_sort | Diogo Pereira |
| collection | DOAJ |
| description | Aerodynamic shape design optimization faces challenges due to the computational demands and the vast design space, limiting its practicality and scalability. While progress has been made in subsonic and transonic regimes, the real-time optimization for supersonic conditions remains unexplored. To bridge this gap, this work exploits knowledge learned from subsonic and transonic real-world data and introduces a rapid optimization framework tailored for the supersonic regime. A novel end-to-end multitask Convolutional Neural Network is proposed to predict the aerodynamic coefficients of an airfoil shape, extracting global and local features directly from the geometry. The surrogate model is thoroughly examined and validated, including an analysis of model explainability. The surrogate model achieves on par results with the state-of-the-art, with relative errors in aerodynamic coefficient predictions below 1.7%. Furthermore, a surrogate-based optimization strategy integrates the surrogate model with a Generative Adversarial Network to generate realistic airfoil shapes, thereby reducing the design space to a low-dimensional representation. This approach provides a robust solution that accelerates the optimization routine by over 3000 times when compared to simulation-based methods while achieving a deviation of less than 1.9% from their optimum performance. Overall, this work strikes a balance between efficiency and effectiveness without compromising reliability. |
| format | Article |
| id | doaj-art-3afe0622a13d47199ec64a6462e6822e |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-3afe0622a13d47199ec64a6462e6822e2025-08-20T02:33:39ZengMDPI AGAerospace2226-43102025-04-0112538910.3390/aerospace12050389End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape OptimizationDiogo Pereira0Frederico Afonso1Fernando Lau2Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalIDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalIDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalAerodynamic shape design optimization faces challenges due to the computational demands and the vast design space, limiting its practicality and scalability. While progress has been made in subsonic and transonic regimes, the real-time optimization for supersonic conditions remains unexplored. To bridge this gap, this work exploits knowledge learned from subsonic and transonic real-world data and introduces a rapid optimization framework tailored for the supersonic regime. A novel end-to-end multitask Convolutional Neural Network is proposed to predict the aerodynamic coefficients of an airfoil shape, extracting global and local features directly from the geometry. The surrogate model is thoroughly examined and validated, including an analysis of model explainability. The surrogate model achieves on par results with the state-of-the-art, with relative errors in aerodynamic coefficient predictions below 1.7%. Furthermore, a surrogate-based optimization strategy integrates the surrogate model with a Generative Adversarial Network to generate realistic airfoil shapes, thereby reducing the design space to a low-dimensional representation. This approach provides a robust solution that accelerates the optimization routine by over 3000 times when compared to simulation-based methods while achieving a deviation of less than 1.9% from their optimum performance. Overall, this work strikes a balance between efficiency and effectiveness without compromising reliability.https://www.mdpi.com/2226-4310/12/5/389aerodynamic shape optimizationsurrogate modelingdeep learningconvolutional neural networksgenerative adversarial networks |
| spellingShingle | Diogo Pereira Frederico Afonso Fernando Lau End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization Aerospace aerodynamic shape optimization surrogate modeling deep learning convolutional neural networks generative adversarial networks |
| title | End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization |
| title_full | End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization |
| title_fullStr | End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization |
| title_full_unstemmed | End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization |
| title_short | End-to-End Deep-Learning-Based Surrogate Modeling for Supersonic Airfoil Shape Optimization |
| title_sort | end to end deep learning based surrogate modeling for supersonic airfoil shape optimization |
| topic | aerodynamic shape optimization surrogate modeling deep learning convolutional neural networks generative adversarial networks |
| url | https://www.mdpi.com/2226-4310/12/5/389 |
| work_keys_str_mv | AT diogopereira endtoenddeeplearningbasedsurrogatemodelingforsupersonicairfoilshapeoptimization AT fredericoafonso endtoenddeeplearningbasedsurrogatemodelingforsupersonicairfoilshapeoptimization AT fernandolau endtoenddeeplearningbasedsurrogatemodelingforsupersonicairfoilshapeoptimization |