发表机构
Mines Paris PSL University（巴黎国立高等矿业学校-巴黎文理研究大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ONECYL基准，含450个高保真仿真和270,000个快照，覆盖层流至湍流，并开发图变换器基线，通过水平集几何编码提升长时程预测精度与泛化能力。
AI 中文摘要
基于图的代理模型为在非结构网格上加速计算流体动力学（CFD）仿真提供了一条有前景的途径。然而，其发展受到跨多个流动状态的基准数据集稀缺以及用于长时程自回归预测的标准化协议缺乏的限制。我们提出了ONECYL（ONE CYLinder），一个针对圆柱绕流的非定常流动的新基准，涵盖层流、过渡流和高雷诺数状态。该基准包含450个高保真变分多尺度有限元仿真（270,000个流动快照），具有随机化的圆柱几何形状，提供时间分辨的速度和压力场，以及网格连通性、几何描述符、雷诺数和积分气动量。除数据集外，ONECYL建立了一个统一的评估框架，结合全场展开误差、虚拟探针以及阻力和升力预测，以评估数值精度和物理保真度。为配合该基准，我们开发了一个图变换器作为参考基线，在非结构网格上自回归预测速度和压力场。利用ONECYL，我们研究了三种雷诺数状态下的几何表示和基于物理的正则化。结果表明，通过水平集表示显式编码圆柱几何形状持续提高了长时程预测精度和对未见几何形状的泛化能力，而基于散度的正则化随着流动复杂性的增加而变得更加有益。ONECYL基准及其图变换器基线为评估基于图的代理模型提供了一个可复现的框架，并为未来非定常钝体绕流长时程预测的研究奠定了基础。
英文摘要
Graph-based surrogate models offer a promising route to accelerate computational fluid dynamics (CFD) simulations on unstructured meshes. However, their development is limited by the scarcity of benchmark datasets spanning multiple flow regimes and standardized protocols for long-horizon autoregressive prediction. We introduce ONECYL (ONE CYLinder), a new benchmark for unsteady flow past a circular cylinder across laminar, transitional, and high-Reynolds-number regimes. The benchmark comprises 450 high-fidelity Variational Multiscale finite-element simulations (270,000 flow snapshots) with randomized cylinder geometries, providing time-resolved velocity and pressure fields together with mesh connectivity, geometric descriptors, Reynolds numbers, and integrated aerodynamic quantities. Beyond the dataset, ONECYL establishes a unified evaluation framework combining full-field rollout errors, virtual probes, and drag and lift predictions to assess numerical accuracy and physical fidelity. To accompany the benchmark, we develop a Graph Transformer as a reference baseline predicting velocity and pressure fields autoregressively on unstructured meshes. Using ONECYL, we investigate geometric representations and physics-based regularization across the three Reynolds-number regimes. The results show that explicitly encoding the cylinder geometry through a level-set representation consistently improves long-horizon prediction accuracy and generalization to unseen geometries, while divergence-based regularization becomes increasingly beneficial as flow complexity increases. The ONECYL benchmark and its Graph Transformer baseline provide a reproducible framework for evaluating graph-based surrogate models and establish a foundation for future research on long-horizon prediction of unsteady bluff-body flows.
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