{
  "id": "1806.05904",
  "version": "v1",
  "published": "2018-06-15T11:09:18.000Z",
  "updated": "2018-06-15T11:09:18.000Z",
  "title": "Machine learning methods for turbulence modeling in subsonic flows over airfoils",
  "authors": [
    "Weiwei Zhang",
    "Linyang Zhu",
    "Jiaqing Kou",
    "Yilang Liu"
  ],
  "comment": "23pages",
  "categories": [
    "physics.flu-dyn",
    "physics.comp-ph"
  ],
  "abstract": "Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism and large discrepancies between models reduce the reliability of simulating complex flows. Therefore, in recent years, data-driven turbulence model has aroused widespread concern in fluid mechanics. Based on the experimental/numerical simulation results, this approach aims to modify or construct the turbulence model for specific purposes by machine learning technologies. In this paper, we take the results calculated by SA model as training data. Different from low Reynolds number turbulent flows, the data from high Reynolds number flows shows an apparent scaling effect, thus leading to difficulties in the data-driven modeling. In order to improve the fitting accuracy, we divided the flow field into near-wall region, wake region, and far-field region, and built individual model for every region. In this paper, we adopted the radial basis function neural network (RBFNN) and some auxiliary optimization algorithms to reconstruct a mapping function between mean variables and the eddy viscosity. Since this model reflects the relationship between local flow characteristics and turbulent eddy viscosity, it is independent on the airfoil shape and flow condition. The training data in this paper is generated from only three subsonic flow calculations of NACA0012 airfoil. By coupling the proposed approach with N-S equations, we calculated various flow cases as well as two different airfoils and showed the eddy viscosity contours, velocity profiles along the normal direction of wall and skin friction coefficient distributions, etc. Compared with the SA model, the results show a reasonable accuracy and better efficiency, which indicates the positive prospect of data-driven methods in turbulence modeling.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-06-15T11:09:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "turbulence model",
      "machine learning methods",
      "subsonic flow",
      "eddy viscosity",
      "radial basis function neural network"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 23,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}