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.

翻译：在汽车空气动力学领域，得益于DrivAerML和DrivAerNet++等数据集，神经代理模型已得到广泛应用，但其主要聚焦于具有大尾流的钝体流动。将这些方法扩展到航空航天领域，尤其是在跨音速领域，仍然面临挑战，这源于可压缩流动的高度非线性以及翼尖涡等三维效应。现有的航空航天数据集主要关注二维翼型，忽略了这些关键的三维现象。为填补这一空白，我们提出了一个新的跨音速三维机翼CFD模拟数据集。该数据集包含约30,000个具有独特几何形状和来流条件的样本的体积场和表面场数据，可用于计算升力和阻力系数，为基于数据的阻力-升力帕累托前沿气动优化奠定基础。我们在数据集上评估了包括Transolver和AB-UPT在内的多种先进神经代理模型，重点关注它们在几何形状和来流变化上的分布外（OOD）泛化能力。AB-UPT在跨音速流场中表现出色，即使对于未见过的机翼构型，也能复现物理一致的阻力-升力帕累托前沿。我们的结果表明，AB-UPT能够近似未见几何形状的阻力-升力帕累托前沿，突显了其作为快速气动设计探索高效工具的潜力。为促进未来研究，我们在https://huggingface.co/datasets/EmmiAI/Emmi-Wing开源了数据集。