{
  "id": "2203.16944",
  "version": "v1",
  "published": "2022-03-31T11:08:54.000Z",
  "updated": "2022-03-31T11:08:54.000Z",
  "title": "A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor",
  "authors": [
    "Stefano Berrone",
    "Davide Oberto"
  ],
  "comment": "26 pages, 13 figures",
  "categories": [
    "physics.flu-dyn",
    "cs.LG",
    "cs.NA",
    "math.NA"
  ],
  "abstract": "In the present paper a new data-driven model to close and increase accuracy of RANS equations is proposed. It is based on the direct approximation of the divergence of the Reynolds Stress Tensor (RST) through a Neural Network (NN). This choice is driven by the presence of the divergence of RST in the RANS equations. Furthermore, once this data-driven approach is trained, there is no need to run any turbulence model to close the equations. Finally, it is well known that a good approximation of a function it is not necessarily a good approximation of its derivative. The architecture and inputs choices of the proposed network guarantee both Galilean and coordinates-frame rotation invariances by looking to a vector basis expansion of the divergence of the RST. Two well-known test cases are used to show advantages of the proposed method compared to classic turbulence models.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-03-31T11:08:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "reynolds stress tensor",
      "data-driven approach",
      "rans models",
      "divergence",
      "rans equations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 26,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}