{
  "id": "2407.15604",
  "version": "v1",
  "published": "2024-07-22T12:59:41.000Z",
  "updated": "2024-07-22T12:59:41.000Z",
  "title": "High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning",
  "authors": [
    "Haokai Wu",
    "Kai Zhang",
    "Dai Zhou",
    "Wen-Li Chen",
    "Zhaolong Han",
    "Yong Cao"
  ],
  "categories": [
    "physics.flu-dyn"
  ],
  "abstract": "This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via \"zero-shot transfer\"), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to DNS results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pre-trained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore EC-SRGAN potential for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-07-22T12:59:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "wall turbulence",
      "generalized zero-shot learning",
      "high-flexibility reconstruction",
      "tbl small-scale velocity fields",
      "high-resolution turbulent boundary layer"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}