{
  "annotation_id": "a4de51d5-ebbb-450e-b4dd-36ea12226508",
  "date_created": "2026-02-17T05:53:12.485000Z",
  "date_modified": "2026-02-17T05:53:12.485000Z",
  "file_hash": "aa6887e96fe87a6f24d17c5dd8c5ca06a08392e99f852db44acb74b73b20b0a6",
  "private": false,
  "record": {
    "abstract": "One of the oldest flow visualization techniques is through multiple pathlines generated by the movement of seeding particles spatially distributed in the flow. In the computerized era, particle images are used in quantitative measurements, such as particle image and particle tracking velocimetry (PIV and PTV). Here, we present several methods for post-processing raw particle images to generate enhanced flow visualization without a need for conducting additional experiments. Three post-processing methods will be shown: 1) controlling the exposure time, 2) color-coding temporal information, and 3) changing the frame of reference. We showcase how employing these three methods can highlight different flow features in three canonical flow cases: vortex ring, leading edge vortex, and turbulent boundary layer. In addition to the quantitative flow field, the multi-pathline visualization is expected to augment our ability to observe fluid flow from many different perspectives.",
    "arxiv_id": "2601.07643",
    "authors": [
      "Yukun Sun",
      "Elijah James",
      "Frank Fang",
      "Jasper Agrawal",
      "Christopher Dougherty",
      "Cong Wang",
      "Chris Roh"
    ],
    "categories": [
      "physics.flu-dyn"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "Multi-pathline flow visualization using PIV images",
    "url": "https://arxiv.org/abs/2601.07643",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5cfc960d-e44f-44e4-ba71-791d64135e1a",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}