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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
ONECYL is a new computational benchmark dataset comprising 450 high-fidelity CFD simulations of unsteady flow past a circular cylinder across multiple Reynolds-number regimes, accompanied by a Graph Transformer reference baseline and a unified evaluation framework for surrogate models. The work is a methodological contribution establishing infrastructure for future research on graph-based CFD surrogate modeling; it does not report clinical outcomes, patient safety data, or validation against real-world applications.
Benchmark dataset and methods paper with baseline model development. Unsteady flow past circular cylinders with randomized geometries across multiple Reynolds-number regimes. Intervention: Graph Transformer baseline model with level-set geometric representation and divergence-based regularization.
Benchmark comprises 450 high-fidelity Variational Multiscale finite-element simulations with 270,000 flow snapshots Level-set geometric representation consistently improves long-horizon prediction accuracy and generalization to unseen cylinder geometries Divergence-based regularization becomes increasingly beneficial as flow complexity increases across laminar, transitional, and high-Reynolds-number regimes
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is a methods paper introducing a benchmark dataset and reference baseline model for graph-based CFD surrogate modeling, with no clinical or patient-relevant outcome, no comparison to established standards, and findings limited to technical performance metrics on a single flow configuration.
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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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