{
  "abstract": "Neural surrogates can accelerate many-query aerodynamic analyses, but their standalone predictions may be insufficiently accurate or robust to support reliable design evaluations. We develop a learned warm-start formulation that predicts mean-flow and turbulence fields directly on native CFD meshes for subsequent correction by the RANS solver. A common architecture and objective structure are used across subcritical and transonic flows, including challenging conditions with shock-induced separation. A condition-modulated Transolver combines Fourier and wall features with target-gated regularization, a Sobolev-type loss, adaptive loss weighting and sampled training. Prediction requires no preliminary flow solution, and training requires no solver-residual evaluation. Extensive 3D CFD evaluations on SuperWing, the NASA Common Research Model (CRM) and DLR-F6 assess geometric and operating-condition generalization, including interpolation, extrapolation and cross-mesh deployment, using Spalart-Allmaras and $k$-$ω$ SST closures on million-cell meshes. Unseen nominal cases achieve geometric-mean iteration speed-ups of approximately $2$-$4\\times$ under a common convergence criterion based on cold-run aerodynamic coefficients. On CRM, learned initialization provides greater aggregate acceleration than nearest-condition restarts using the same solution library. Resolution invariance enables direct deployment of coarse-mesh models on finer meshes without retraining, solution interpolation or target-condition coarse solves. Including data generation and training, estimated campaign speed-ups reach $2.1$-$2.3\\times$. No warm run diverges; fewer than $2\\%$ fail to satisfy the cold-reference convergence criterion. These results support learned initialization for repeated aerodynamic analyses, while highlighting initialization sensitivity near separation as a remaining limitation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01240",
  "author_total": 3,
  "authors": [
    {
      "name": "Muhammad Irfan Zafar"
    },
    {
      "name": "Saleh Abuhanieh"
    },
    {
      "name": "Şahin Yiğit"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0wex3dgy6jzhz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01240",
  "fulltext_url": null,
  "id": "0wex3dgy6jzhz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01240",
  "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.CE"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01240",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0wex3dgy6jzhz.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.01240",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01240"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Learned Warm Starts for Steady Transonic RANS Simulations of Wing and Aircraft Configurations",
  "title_text": "Learned Warm Starts for Steady Transonic RANS Simulations of Wing and Aircraft Configurations",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/0wex3dgy6jzhz",
  "venue": "arXiv",
  "versions": []
}