{
  "abstract": "Foil windsurfing is a sport in which competitors fine-tune their setup to optimize performance under varying environmental conditions. However, such adjustments are typically guided by intuition. This paper presents a methodology for improving hydrofoil design using computational fluid dynamics. First, structured light scanning was used to obtain the geometry of a conventional hydrofoil. Subsequently, five derived designs have been explored, inspired by advancements in airplane wing design. Following this, a preliminary design was selected, and a design of experiments approach was implemented. Generated simulation data were used to train a machine learning surrogate, which is ultimately used in the multi-objective design optimization. While the surrogate-based approach yielded promising results, it was limited to exploring a restricted set of design parameters. To overcome this constraint, additional improvements were attained through adjoint optimization, demonstrating that comparable performance can be achieved with a distinctly different hydrofoil design. Finally, the conventional design was refined through adjoint optimization. Notably, adjoint optimization yielded a 15% increase in efficiency. The results suggest that both proposed approaches can produce novel hydrofoil designs capable of outperforming the original configuration under specific conditions; however, due to its inherent flexibility, the adjoint approach with realistic constraints offers more freedom in the design exploration process.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Ocean Engineering 347 (2026) 123943",
  "arxiv": "2611.06183",
  "author_total": 4,
  "authors": [
    {
      "name": "Ante Sikirica"
    },
    {
      "name": "Ivana Lučin"
    },
    {
      "name": "Marta Alvir"
    },
    {
      "name": "Tin Matulja"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/63jaq699qjc2q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06183",
  "fulltext_url": null,
  "id": "63jaq699qjc2q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06183",
  "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",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06183",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/63jaq699qjc2q.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:21:00Z",
      "native_id": "2611.06183",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06183"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Improving Hydrofoil Efficiency Through Simulation-Driven Design Optimization",
  "title_text": "Improving Hydrofoil Efficiency Through Simulation-Driven Design Optimization",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:30Z",
  "url": "https://research.draconitic.com/paper/63jaq699qjc2q",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.oceaneng.2025.123943",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}