{
  "abstract": "High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the initial flow-development period required to reach statistically stationary conditions. This work investigates the use of a Super-Resolution Graph Neural Network (SR-GNN) to improve the initialization of high-order NekRS simulations. Lower-order P = 2 velocity fields are used as inputs to reconstruct higher-order representations, which are then used as initial conditions for P = 7 restart simulations. The approach is evaluated using a 146-pebble bed at Re = 1000, Re = 2500, and Re = 5000, with pressure-drop convergence used as the main figure of merit. The SR-GNN models were trained using paired low- and high-order snapshots and were first evaluated through qualitative inference comparisons. High-order restart simulations showed that, for Re = 1000 and Re = 2500, the SR-GNN initialized cases produced pressure-drop histories similar to direct restarts from true P = 2 fields. For Re = 5000, however, the super-resolved field restart approached the statistically stationary P = 7 pressure-drop range faster than both the direct P = 2 restart and the reference P = 7 simulation initialized from a uniform velocity field. The trained Re = 5000 model was also applied to a larger 1568-pebble bed, demonstrating qualitative applicability of the workflow to a significantly larger packed-bed geometry. These results indicate that SR-GNN-based initialization is a promising strategy for reducing high-order flow-development cost, while also motivating further work on broader Reynolds-number and geometry generalization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02656",
  "author_total": 16,
  "authors": [
    {
      "name": "Guilherme Gottems"
    },
    {
      "name": "Vasil Ivakimov"
    },
    {
      "name": "Luiz Aldeia Machado"
    },
    {
      "name": "Mahmoud Yaseen"
    },
    {
      "name": "Tri Nguyen"
    },
    {
      "name": "Elia Merzari"
    },
    {
      "name": "Riccardo Balin"
    },
    {
      "name": "Dillon Shaver"
    },
    {
      "name": "Filippo Simini"
    },
    {
      "name": "Bethany Lusch"
    },
    {
      "name": "Venkatram Vishwanath"
    },
    {
      "name": "Haomin Yuan"
    },
    {
      "name": "Misun Min"
    },
    {
      "name": "Ramesh Balakrishnan"
    },
    {
      "name": "Jun Fang"
    },
    {
      "name": "Paul Fischer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8b4tsx81nccah.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02656",
  "fulltext_url": null,
  "id": "8b4tsx81nccah",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02656",
  "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",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7207846130",
  "pdf_url": "https://arxiv.org/pdf/2609.02656",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8b4tsx81nccah.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02656",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02656"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Super-Resolution Initialization of High-Fidelity CFD Simulations for Pebble-Bed Reactors",
  "title_text": "Super-Resolution Initialization of High-Fidelity CFD Simulations for Pebble-Bed Reactors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/8b4tsx81nccah",
  "venue": "arXiv",
  "versions": []
}