OptiWing3D is the first publicly available dataset of high-fidelity shape optimized 3D wing geometries. Existing aerodynamics datasets are either limited to 2D simulations, lack optimization, or derive diversity solely from perturbations to a single baseline design, constraining their application as benchmarks to inverse design approaches and in the study of design diversity. The OptiWing3D dataset addresses these gaps, consisting of 1552 simulations resulting in 776 wing designs initialized from distinct extruded airfoil cross-sections. Additionally, a majority of the optimized wings in the dataset are paired to 2D counterparts optimized under identical conditions, creating the first multi-fidelity aerodynamic shape optimization dataset. Moreover, this structure allows for a direct comparison between 2D and 3D aerodynamic simulations. It is observed that 3D optimized designs diverge most prominently from the 2D-optimized designs near the wingtip, where three-dimensional effects are strongest, a finding made possible by the paired nature of the dataset. Finally, we demonstrate a constraint-aware conditional latent diffusion model capable of generating optimized wings from flow conditions, establishing a baseline for future inverse design approaches. The dataset, containing wing geometries and surface pressure distributions is publicly released to advance research in data-driven aerodynamic design.

翻译：OptiWing3D是首个公开可用的高保真形状优化三维机翼几何数据集。现有的空气动力学数据集要么局限于二维仿真，缺乏优化过程，要么仅通过对单一基准设计的扰动来获得多样性，这限制了它们作为逆向设计方法基准以及设计多样性研究工具的应用。OptiWing3D数据集弥补了这些不足，包含1552次仿真生成的776个机翼设计，这些设计均源自不同的翼型截面拉伸初始化。此外，数据集中大多数优化机翼均配有在相同条件下优化的二维对应设计，从而创建了首个多保真度空气动力学形状优化数据集。这种结构还允许对二维和三维空气动力学仿真进行直接比较。研究发现，三维优化设计与二维优化设计在翼尖附近差异最为显著，该区域三维效应最强，这一发现得益于数据集的配对特性。最后，我们展示了一种约束感知的条件潜在扩散模型，该模型能够根据流动条件生成优化机翼，为未来的逆向设计方法建立了基准。该数据集包含机翼几何形状和表面压力分布，现已公开发布以推动数据驱动的空气动力学设计研究。