{
  "annotation_id": "007bf019-6b33-47b5-9403-2322299835b9",
  "date_created": "2026-02-17T05:53:05.111000Z",
  "date_modified": "2026-02-17T05:53:05.111000Z",
  "file_hash": "3b4c0baa5328e20a348b8edf29d380f3ac24e543565d4976dcb40c4609d92b60",
  "private": false,
  "record": {
    "abstract": "Accurate prediction of flow resistance in high-Froude-number aerated flows remains challenging due to air entrainment, which causes strong spatial variability in mixture density. In this work, we introduce a density-weighted (Favre) averaging approach to rigorously account for vertical distributions of air concentration and velocity. Favre averaging naturally captures variations in mixture density induced by air entrainment, thereby enabling a density-consistent Shallow Water Equation (SWE) formulation for aerated flows. Within this framework, we present a novel Darcy-Weisbach friction factor formulation that decomposes contributions associated with uniform flow, spatially varying flow, and temporally evolving flow, and incorporates momentum and pressure correction factors reflecting the vertical structure of the mixture. Application to experimental data from the literature demonstrates that the Favre-averaged SWE framework provides a physically consistent means of quantifying effective friction. Overall, this work establishes a mechanistic, density-weighted methodology for modelling resistance in high-Froude-number aerated flows, provides new physical insight into the role of aeration in frictional dissipation, and lays a rational foundation for future modelling of unsteady and rapidly varied aerated flows.",
    "arxiv_id": "2601.05523",
    "authors": [
      "Matthias Kramer"
    ],
    "categories": [
      "physics.flu-dyn"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "A Favre-Averaged Shallow Water Framework for Aerated Flows with Friction Factor Decomposition",
    "url": "https://arxiv.org/abs/2601.05523",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e49faa0e-0bb7-419d-8aa2-099bda95d275",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}