{
  "abstract": "Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds a flow absent from existing automotive aerodynamics datasets such as DrivAerML and SHIFT-SUV: the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting drag lowers fuel efficiency, raises emissions and limits the range of electric trucks. Scale-resolved Computational Fluid Dynamics (CFD) is too costly for broad design exploration; neural surrogates can predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity, domain-specific data. We introduce SHIFT-Truck, the first such dataset for pickup trucks. It comprises 1,000 Spalart-Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters. Each case is run on a mesh of about 100 million cells at a Reynolds number of $1.4 \\times 10^7$ and released with time-averaged surface pressure, wall shear stress, volumetric pressure and velocity. The setup is verified by grid refinement and repeated runs, and checked against wind-tunnel measurements. We define geometry-grouped splits and benchmark four neural surrogates, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. SHIFT-Truck also introduces controlled distribution shifts in the operating point, the input surface discretization and the vehicle archetype. Models with strong in-distribution performance can degrade substantially under these shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. SHIFT-Truck is thus a benchmark not only for surrogate accuracy but also for generalization across physical and numerical distributions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38638",
  "author_total": 7,
  "authors": [
    {
      "name": "Riddhiman Raut"
    },
    {
      "name": "Yin Yu"
    },
    {
      "name": "Aashwin Anand Mishra"
    },
    {
      "name": "Michael Emory"
    },
    {
      "name": "Thomas Economon"
    },
    {
      "name": "Peter Lyu"
    },
    {
      "name": "Juan J. Alonso"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/18dnphxb1ea8n.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38638",
  "fulltext_url": null,
  "id": "18dnphxb1ea8n",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38638",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "2202|Aerodynamics and Fluid Dynamics Research"
    ]
  ],
  "openalex": "W7215035331",
  "pdf_url": "https://arxiv.org/pdf/2609.38638",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/18dnphxb1ea8n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.38638",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38638"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks",
  "title_text": "SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/18dnphxb1ea8n",
  "venue": "arXiv",
  "versions": []
}