{
  "id": "2011.04632",
  "version": "v1",
  "published": "2020-11-09T18:43:27.000Z",
  "updated": "2020-11-09T18:43:27.000Z",
  "title": "Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: a computation and data-driven approach",
  "authors": [
    "Karthik Menon",
    "Rajat Mittal"
  ],
  "categories": [
    "physics.flu-dyn",
    "physics.comp-ph",
    "physics.data-an"
  ],
  "abstract": "A physics-based data-driven computational framework for the quantitative analysis of vortex kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows are characterized by the dominant influence of a small number of vortex structures, but the complexity of these flows makes it difficult to conduct a quantitative analysis of this influence at the level of individual vortices. The method presented here combines machine learning-inspired clustering methods with a rigorous mathematical partitioning of aerodynamic loads to enable detailed quantitative analysis of vortex kinematics and vortex-induced aerodynamic loads. We demonstrate the utility of this approach by applying it to an ensemble of 165 distinct Navier-Stokes simulations of flow past a sinusoidally pitching airfoil. Insights enabled by the current methodology include the identification of a period-doubling route to chaos in this flow, and the precise quantification of the role that leading-edge vortices play in driving aeroelastic pitch oscillations.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-09T18:43:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantitative analysis",
      "individual vortices",
      "induced aerodynamic loading",
      "data-driven approach",
      "vortex-dominated flows"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}