Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design
Recent advances in contrastive language‒image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language inter...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Theoretical and Applied Mechanics Letters |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095034925000340 |
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| author | Mingcheng Lei Yufei Zhang |
| author_facet | Mingcheng Lei Yufei Zhang |
| author_sort | Mingcheng Lei |
| collection | DOAJ |
| description | Recent advances in contrastive language‒image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language interfaces. To the authors’ knowledge, this study establishes the first end-to-end, bidirectional mapping between textual descriptions (e.g., “low-drag supercritical wing for transonic conditions”) and parametric airfoil geometries represented by class-shape transformation parameters. The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning, along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations. The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels. This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering. |
| format | Article |
| id | doaj-art-253ffb64bcd4440faa95122a6f6a93b8 |
| institution | Matheson Library |
| issn | 2095-0349 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Theoretical and Applied Mechanics Letters |
| spelling | doaj-art-253ffb64bcd4440faa95122a6f6a93b82025-07-13T04:54:12ZengElsevierTheoretical and Applied Mechanics Letters2095-03492025-09-01155100602Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic designMingcheng Lei0Yufei Zhang1School of Aerospace Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Aerospace Engineering, Tsinghua University, Beijing 100084, China; State Key Laboratory of Advanced Space Propulsion, Tsinghua University, Beijing 100084, China; Corresponding author.Recent advances in contrastive language‒image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language interfaces. To the authors’ knowledge, this study establishes the first end-to-end, bidirectional mapping between textual descriptions (e.g., “low-drag supercritical wing for transonic conditions”) and parametric airfoil geometries represented by class-shape transformation parameters. The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning, along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations. The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels. This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.http://www.sciencedirect.com/science/article/pii/S2095034925000340Airfoil designContrastive learningNatural language processingGenerative modelClass-shape transformation |
| spellingShingle | Mingcheng Lei Yufei Zhang Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design Theoretical and Applied Mechanics Letters Airfoil design Contrastive learning Natural language processing Generative model Class-shape transformation |
| title | Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design |
| title_full | Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design |
| title_fullStr | Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design |
| title_full_unstemmed | Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design |
| title_short | Generating airfoils from text: FoilCLIP, A novel framework for language-conditioned aerodynamic design |
| title_sort | generating airfoils from text foilclip a novel framework for language conditioned aerodynamic design |
| topic | Airfoil design Contrastive learning Natural language processing Generative model Class-shape transformation |
| url | http://www.sciencedirect.com/science/article/pii/S2095034925000340 |
| work_keys_str_mv | AT mingchenglei generatingairfoilsfromtextfoilclipanovelframeworkforlanguageconditionedaerodynamicdesign AT yufeizhang generatingairfoilsfromtextfoilclipanovelframeworkforlanguageconditionedaerodynamicdesign |