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arXiv:2611.17152 · v1

AeroZoo: A Benchmark of Well-Posed Flow Problems for Foundation Models of Aerodynamics

AeroZoo：面向空气动力学基础模型的良定流动问题基准

Peter SharpeCarmelo GonzalesCorey AdamsMehdi AtaeiSanjay Choudhry
cs.LGcs.CEphysics.flu-dyn
首次提交 2026-11-13 01:36
版本更新 2026-11-13 01:36
雷达观测 2026-11-13 14:15
arXiv ↗PDF ↗

摘要

Neural surrogates for computational fluid dynamics (CFD) are trained and ranked on datasets that each vary one design family in one flow domain. Such datasets cannot test the least that an aerodynamic foundation model must do: predict CFD-quality incompressible flow in seconds for new shapes in new enclosures. On DrivAerML, larger models and longer training barely lower Transolver's error; doubling the training cases cuts it by about 12% up to 400 cases, exhausting the dataset. We introduce AeroZoo, a benchmark and training corpus of 3,815 time-mean solutions to the unsteady Reynolds-averaged Navier--Stokes (RANS) equations with a transition model, across seven geometry families, eight enclosure configurations and Reynolds numbers from 10310^3 to 10810^8. Each sample poses a well-posed PDE problem in the form a solver receives it: the boundary meshes grouped by condition type, the prescribed values and the fluid properties. The 2,816 samples with a stable time mean form the canonical benchmark: two tasks, engineering metrics and 22 frozen splits, disjoint in geometry, that hold out geometry families, enclosures, boundary-condition layouts and Reynolds ranges. On the random split, boundary-condition tokens cut GeoTransolver's median velocity error from 76.9% to 8.4% of the inlet speed in single-seed runs (uniform inlet flow: 11.0%). ISLA, a transformer for boundary-driven PDEs, reaches 5.3% over four seeds, about 4 times the change from a finer mesh. On the same test samples, a held-out geometry family barely changes ISLA's error relative to uniform flow, and a held-out enclosure or boundary-condition layout raises it 2.4 to 9.2 times. The data and the scoring code will be released.

作者备注：35 pages, 13 figures, 23 tables. Lead authors: Peter Sharpe and Carmelo Gonzales. Companion paper to "ISLA: A Coordinate-Free, Measure-Weighted Transformer for Boundary-Driven PDEs" (Sharpe, 2026). Dataset to be published shortly at https://huggingface.co/datasets/nvidia/AeroZoo

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AI 论文解读

基于摘要 · 非全文审阅

作者提出 AeroZoo 基准与训练语料，用 3,815 个 RANS 时均解覆盖多几何族、多外罩配置与宽雷诺数范围，并设计按几何族、外罩、边界条件布局和雷诺数范围留出的冻结划分，以检验空气动力学基础模型的跨分布泛化能力。

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v1 · 2026-11-13 01:36 · 最新观测版本

AeroZoo: A Benchmark of Well-Posed Flow Problems for Foundation Models of Aerodynamics

Neural surrogates for computational fluid dynamics (CFD) are trained and ranked on datasets that each vary one design family in one flow domain. Such datasets cannot test the least that an aerodynamic foundation model must do: predict CFD-quality incompressible flow in seconds for new shapes in new enclosures. On DrivAerML, larger models and longer training barely lower Transolver's error; doubling the training cases cuts it by about 12% up to 400 cases, exhausting the dataset. We introduce AeroZoo, a benchmark and training corpus of 3,815 time-mean solutions to the unsteady Reynolds-averaged Navier--Stokes (RANS) equations with a transition model, across seven geometry families, eight enclosure configurations and Reynolds numbers from 10310^3 to 10810^8. Each sample poses a well-posed PDE problem in the form a solver receives it: the boundary meshes grouped by condition type, the prescribed values and the fluid properties. The 2,816 samples with a stable time mean form the canonical benchmark: two tasks, engineering metrics and 22 frozen splits, disjoint in geometry, that hold out geometry families, enclosures, boundary-condition layouts and Reynolds ranges. On the random split, boundary-condition tokens cut GeoTransolver's median velocity error from 76.9% to 8.4% of the inlet speed in single-seed runs (uniform inlet flow: 11.0%). ISLA, a transformer for boundary-driven PDEs, reaches 5.3% over four seeds, about 4 times the change from a finer mesh. On the same test samples, a held-out geometry family barely changes ISLA's error relative to uniform flow, and a held-out enclosure or boundary-condition layout raises it 2.4 to 9.2 times. The data and the scoring code will be released.

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