Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914302830575616 |
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| author | Paischer, Fabian Cotteleer, Leo Dreze, Yann Kurle, Richard Rubini, Dylan Bleeker, Maurits Kronlachner, Tobias Brandstetter, Johannes |
| author_facet | Paischer, Fabian Cotteleer, Leo Dreze, Yann Kurle, Richard Rubini, Dylan Bleeker, Maurits Kronlachner, Tobias Brandstetter, Johannes |
| contents | The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such as wingtip vortices. Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions. This allows computation of lift and drag coefficients, providing a foundation for data-driven aerodynamic optimization of the drag-lift Pareto front. We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT, focusing on their out-of-distribution (OOD) generalization over geometry and inflow variations. AB-UPT demonstrates strong performance for transonic flowfields and reproduces physically consistent drag-lift Pareto fronts even for unseen wing configurations. Our results demonstrate that AB-UPT can approximate drag-lift Pareto fronts for unseen geometries, highlighting its potential as an efficient and effective tool for rapid aerodynamic design exploration. To facilitate future research, we open-source our dataset at https://huggingface.co/datasets/EmmiAI/Emmi-Wing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21474 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes Paischer, Fabian Cotteleer, Leo Dreze, Yann Kurle, Richard Rubini, Dylan Bleeker, Maurits Kronlachner, Tobias Brandstetter, Johannes Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such as wingtip vortices. Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions. This allows computation of lift and drag coefficients, providing a foundation for data-driven aerodynamic optimization of the drag-lift Pareto front. We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT, focusing on their out-of-distribution (OOD) generalization over geometry and inflow variations. AB-UPT demonstrates strong performance for transonic flowfields and reproduces physically consistent drag-lift Pareto fronts even for unseen wing configurations. Our results demonstrate that AB-UPT can approximate drag-lift Pareto fronts for unseen geometries, highlighting its potential as an efficient and effective tool for rapid aerodynamic design exploration. To facilitate future research, we open-source our dataset at https://huggingface.co/datasets/EmmiAI/Emmi-Wing. |
| title | Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.21474 |