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ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows — arXiv:2609.08947

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08947
License (as archived): CC0-1.0
Archived: 2026-10-16T14:02:35.376Z
SHA-256 of the archived text: cc9505e83b2f67f75bd77376afc8e74e21d035e65e8e068c5fe7da30a1359516
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How to cite

ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows — arXiv:2609.08947 — arXiv (en), https://arxiv.org/abs/2609.08947 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_febe7bb8960a (Metraipolis archive dc_febe7bb8960a, sha256 cc9505e83b2f67f75bd77376afc8e74e21d035e65e8e068c5fe7da30a1359516)

BibTeX

@misc{dcfebe7bb8960a,
  title = {ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows — arXiv:2609.08947},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08947},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_febe7bb8960a; sha256 cc9505e83b2f67f75bd77376afc8e74e21d035e65e8e068c5fe7da30a1359516},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08947. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.LG).
Categorie arXiv: Fluid Dynamics (physics.flu-dyn) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.08947 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:39:43.301Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).