{
  "abstract": "HiLiftAeroML is, to our knowledge, the first open high-fidelity computational fluid dynamics dataset dedicated to high-lift aircraft aerodynamics. It contains 1,800 simulations spanning 180 variants of the NASA Common Research Model high-lift configuration and ten angles of attack from $4^\\circ$ to $22^\\circ$. Each case was generated with a GPU-accelerated explicit wall-modeled large-eddy simulation approach on solution-adapted grids of 300--500 million cells, covering attached, separated, and post-stall flow conditions. Comparisons with wind-tunnel measurements for reference landing configurations show good agreement in integrated loads and sectional pressures, with grid adaptation substantially improving drag and pitching-moment predictions. The CC-BY-4.0 release includes geometries, time-averaged surface and volume fields, integrated loads, validation material, and deterministic benchmark splits. Initial GeoTransolver and Transolver baselines, evaluated on the complete native surface and volume support of every held-out case rather than on a sampled subset, reconstruct the fields well for several interpolation and held-out-geometry tests, while high-angle separated flow, limited-data training, and out-of-distribution flow-regime shifts remain substantially harder. The dataset and baselines provide a common resource for developing and assessing data-driven models for realistic high-lift aerodynamics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.19565",
  "author_total": 14,
  "authors": [
    {
      "name": "Neil Ashton"
    },
    {
      "name": "Adam Clark"
    },
    {
      "name": "Konrad Goc"
    },
    {
      "name": "Liam Heidt"
    },
    {
      "name": "Christopher Ivey"
    },
    {
      "name": "Rahul Agrawal"
    },
    {
      "name": "Sanjeeb T Bose"
    },
    {
      "name": "Corey Adams"
    },
    {
      "name": "Peter Sharpe"
    },
    {
      "name": "Daniel Leibovici"
    },
    {
      "name": "Semih Akkurt"
    },
    {
      "name": "Sheel Nidhan"
    },
    {
      "name": "Rishi Ranade"
    },
    {
      "name": "Jean Kossaifi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6wt676mz01apw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.19565",
  "fulltext_url": null,
  "id": "6wt676mz01apw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.19565",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "2206|Computational Fluid Dynamics and Aerodynamics"
    ]
  ],
  "openalex": "W7161853035",
  "pdf_url": "https://arxiv.org/pdf/2605.19565",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6wt676mz01apw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2605.19565",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.19565"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "HiLiftAeroML: A High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics",
  "title_text": "HiLiftAeroML: A High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:52Z",
  "url": "https://research.draconitic.com/paper/6wt676mz01apw",
  "venue": "arXiv",
  "versions": []
}