{
  "abstract": "This study presents an intelligent numerical framework for predicting the unsteady squeezing flow of a bioconvective nanofluid containing gyrotactic microorganisms between two parallel plates. The effects of second-order velocity slip, higher-order chemical reaction, Brownian motion, thermophoresis, and bioconvective transport are incorporated into the mathematical formulation. The governing nonlinear partial differential equations are transformed into a coupled system of ordinary differential equations using suitable similarity transformations and are then solved numerically through the Runge–Kutta–Fehlberg shooting method. The obtained numerical solutions are used to construct high-quality datasets for training, validating, and testing a Back-Propagated Levenberg–Marquardt Scheme Artificial Neural Network (BLMS-ANN). The physical results reveal that the squeezing parameter significantly redistributes the velocity field and enhances core-region momentum transport, while the slip parameters strongly modify the near-wall velocity gradient and shear stress. Increasing the chemical reaction parameter suppresses nanoparticle concentration, whereas thermophoresis enhances the temperature field but reduces concentration distribution. Moreover, the Peclet number plays a dominant role in controlling microorganism transport by strengthening convective effects relative to diffusion. The developed BLMS-ANN model demonstrates excellent predictive capability, with very low validation mean squared errors, correlation coefficients of R = 1, and residual errors tightly clustered around the zero-error line. The developed BLMS-ANN model reduced the computational time from 4.1 s required by the RKF45 solver to 1.1 s for prediction, corresponding to a speed-up factor of approximately 3.73 and a computational time reduction of 73.17%. These findings confirm that the proposed intelligent framework provides a highly accurate, stable, and computationally efficient surrogate model for predicting complex nonlinear transport phenomena in bioconvective nanofluid squeezing flows.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran; Corresponding author.",
    "Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran",
    "Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran; Department of Computer Engineering, Biruni University, Istanbul 34010, Turkey; Corresponding author. Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mehdi Mahboobtosi"
    },
    {
      "affs": [
        1
      ],
      "name": "Ali Gholami"
    },
    {
      "affs": [
        1
      ],
      "name": "Fateme Nadalinia Chari"
    },
    {
      "affs": [
        2
      ],
      "name": "Davood Domiri Ganji"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bkmkt5384g9n7.bib",
  "cited_by": 0,
  "doi": "10.1016/j.rsurfi.2026.101022",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2666845926003144",
  "id": "bkmkt5384g9n7",
  "issns": [
    "2666-8459"
  ],
  "landing_url": "https://doi.org/10.1016/j.rsurfi.2026.101022",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "TP250-261"
    ],
    [
      "openalex",
      "2204|Nanofluid Flow and Heat Transfer"
    ]
  ],
  "openalex": "W7222975235",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/bkmkt5384g9n7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:38Z",
      "native_id": "fa970971c8cc4843a080eb5c53cb5316",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.rsurfi.2026.101022"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "lcc:TP250-261",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Intelligent RKF–BLMS-ANN prediction of unsteady bioconvective nanofluid flow containing swimming gyrotactic microorganisms under HOC reaction and SOVS effects",
  "title_text": "Intelligent RKF–BLMS-ANN prediction of unsteady bioconvective nanofluid flow containing swimming gyrotactic microorganisms under HOC reaction and SOVS effects",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:38Z",
  "url": "https://research.draconitic.com/paper/bkmkt5384g9n7",
  "venue": "Results in Surfaces and Interfaces",
  "versions": []
}