{
  "abstract": "Next-generation aircraft, rotorcraft, and wind turbines demand improved aerodynamic efficiency, making drag reduction a central design objective; in transition-sensitive configurations, the extent of laminar flow strongly affects viscous drag and performance. Accurate prediction of laminar-turbulent transition is therefore essential. Transport-equation-based transition models, however, increase computational cost and implementation complexity. Neural-network-based closures can be difficult to interpret and integrate into independent flow solvers, while a single compact correction trained on heterogeneous transition data may fail to preserve mechanism-specific behavior. This study develops a mechanism-separated transition model using field inversion and symbolic regression. The framework treats natural, crossflow, and separation-induced transition with separate correction branches and yields explicit, closed-form corrections to the Spalart-Allmaras production term, without additional transport equations or runtime neural-network inference. The closed-form model is implemented in an independent flow solver to assess implementation portability and is evaluated on canonical cases and complex three-dimensional configurations, including a natural-laminar-flow transport wing and a hovering rotor. Across the tested cases, the model captures the principal transition-front trends and associated aerodynamic-performance changes. For the hovering-rotor case, it requires about 55% of the wall-clock time of the comparable transport-equation transition model.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.24162",
  "author_total": 3,
  "authors": [
    {
      "name": "Seunghyun Joo"
    },
    {
      "name": "Younghyo Kim"
    },
    {
      "name": "Kwanjung Yee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1z6rezwv1hhxg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24162",
  "fulltext_url": null,
  "id": "1z6rezwv1hhxg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24162",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "2206|Computational Fluid Dynamics and Aerodynamics"
    ]
  ],
  "openalex": "W7214048128",
  "pdf_url": "https://arxiv.org/pdf/2609.24162",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1z6rezwv1hhxg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.24162",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24162"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression",
  "title_text": "Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/1z6rezwv1hhxg",
  "venue": "arXiv",
  "versions": []
}