{
  "abstract": "We develop a physics-informed method for aerodynamic shape optimisation of turbine blades, treating the flow field and blade geometry as coupled unknowns. The method translates prescribed surface-pressure distributions into geometric modifications through pressure-target continuation and alternating flow-geometry updates. A differentiable representation of the blade surfaces allows their positions and orientations to be optimised within the current flow field, followed by flow re-adaptation to the modified boundaries. The inverse formulation uses the two-dimensional steady compressible Euler equations as physics constraints and is evaluated on the LS89 turbine cascade using 28 pressure-target points derived from experimental data. Starting from a modified profile and a warm-started flow network, the final pressure-side and suction-side root-mean-square fitting errors are 1.13% and 2.21% of inlet total pressure, respectively. Independent forward verification with SharC reproduces the principal surface-pressure trends of the experimental measurements, with remaining differences near the trailing edge. These results demonstrate the translation of experimental-origin loading data into geometric blade modifications through coordinated optimisation of the flow field and moving boundaries.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.05691",
  "author_total": 2,
  "authors": [
    {
      "name": "Yuan Huang"
    },
    {
      "name": "Francesca di Mare"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/db9e7a64e2824.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.05691",
  "fulltext_url": null,
  "id": "db9e7a64e2824",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.05691",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2611.05691",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/db9e7a64e2824.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:20:56Z",
      "native_id": "2611.05691",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.05691"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "A physics-informed neural network method for pressure-driven turbine-blade optimization",
  "title_text": "A physics-informed neural network method for pressure-driven turbine-blade optimization",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:56Z",
  "url": "https://research.draconitic.com/paper/db9e7a64e2824",
  "venue": "arXiv",
  "versions": []
}