{
  "abstract": "This study investigates shape-dependent thermal transport in a blood-based Cu–Al₂O₃–Au trihybrid nanofluid using a local throat-level similarity model motivated by stenosed arterial geometry. The model incorporates magnetohydrodynamics, thermal radiation, viscous dissipation, porous-medium resistance, Darcy–Forchheimer drag, and Cattaneo–Christov non-Fourier heat-flux. The key novelty lies in combining mixed-shape nanoparticle modeling with response surface methodology (RSM) optimization and artificial neural network (ANN) surrogate prediction for arterial-flow configurations relevant to thermal therapy. Shape-dependent property correlations compare a fully spherical trihybrid suspension with a mixed-shape case (spherical Cu/Al₂O₃, cylindrical Au). Using similarity transformations, the governing equations are converted into coupled nonlinear ODEs and solved numerically with a collocation-based boundary-value solver. Parametric analysis covers magnetic parameter (0 ≤ M ≤ 3), Eckert number (0 ≤ Ec ≤ 1.5), radiation parameter (0 ≤ Rd ≤ 0.75), Darcy parameter (1 ≤ K ≤ 5), thermal relaxation parameter (0 ≤ αt ≤ 0.1), and nanoparticle volume fraction (0.03 ≤ ϕ ≤ 0.1). RSM with a face-centered central composite design applied over the bounded optimization space for prediction of Nusselt number (Nu), followed by feedforward ANN, identifies the high-M, high-ϕ, and low-Rd region as favorable for local wall heat-transfer enhancement. The solver-confirmed constrained optimum (M = 2.9935, ϕ = 0.0525, and Rd = 0.00012) yields Nu = 12.286, with ANN predicting Nu = 12.1721 (relative deviation: 0.93%). The mixed shape configuration increase mean velocity by 0.56–6.18% and mean temperature by 0.49–12.88% versus spherical particles. Overall, the combined numerical–RSM–ANN framework offers a reproducible route for screening design space and optimizing trihybrid nanofluid heat transfer in magnetically assisted arterial thermal-therapy modeling.\n\nClinical trial number: Not applicable. This study did not involve a clinical trial.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632014 India"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sakshi Kumari",
      "orcid": "0009-0003-5618-1605"
    },
    {
      "affs": [
        0
      ],
      "name": "Rajesh Bhatt",
      "orcid": "0000-0003-2012-3035"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cmwgb2j85cnqb.bib",
  "cited_by": 0,
  "doi": "10.1186/s11671-026-05085-5",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13731580/",
  "id": "cmwgb2j85cnqb",
  "issns": [
    "2731-9229"
  ],
  "landing_url": "https://doi.org/10.1186/s11671-026-05085-5",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "1507|Nanofluid Flow and Heat Transfer"
    ]
  ],
  "openalex": "W7225191031",
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s11671-026-05085-5.pdf",
  "pmcid": "PMC13731580",
  "pmid": "42975777",
  "published": "2026-11-03",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/cmwgb2j85cnqb.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:21:45Z",
      "native_id": "PMC13731580",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s11671-026-05085-5"
    }
  ],
  "subjects": [],
  "title": "ANN validation and RSM optimization of Cattaneo-Christov heat flux in MHD stenotic artery trihybrid nanofluid flow",
  "title_text": "ANN validation and RSM optimization of Cattaneo-Christov heat flux in MHD stenotic artery trihybrid nanofluid flow",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:45Z",
  "url": "https://research.draconitic.com/paper/cmwgb2j85cnqb",
  "venue": "Discover Nano",
  "versions": []
}