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ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control — arXiv:2610.41304

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41304
License (as archived): CC0-1.0
Archived: 2026-11-03T07:12:41.912Z
SHA-256 of the archived text: d60b5c4665da523ceb271cb9360f7abc39be23fe6ec123109aed3e49c446bfbd
Stable URL of this page: https://metraipolis.com/documento/dc_3224bbbbf429

How to cite

ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control — arXiv:2610.41304 — arXiv (en), https://arxiv.org/abs/2610.41304 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3224bbbbf429 (Metraipolis archive dc_3224bbbbf429, sha256 d60b5c4665da523ceb271cb9360f7abc39be23fe6ec123109aed3e49c446bfbd)

BibTeX

@misc{dc3224bbbbf429,
  title = {ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control — arXiv:2610.41304},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41304},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3224bbbbf429; sha256 d60b5c4665da523ceb271cb9360f7abc39be23fe6ec123109aed3e49c446bfbd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41304. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Particle image velocimetry (PIV) measures fluid motion from images of tracer particles, but its accuracy depends on choices throughout the estimation pipeline, including image preprocessing, flow estimation, and postprocessing. These choices are typically left to expert tuning, and a fixed configuration can become less accurate as conditions change. This matters especially when PIV supplies feedback for real-time control, where measurements must be both accurate and fast, and flow conditions vary. We introduce ART of PIV -- short for Agentic Real-Time and also a nod to the art of tuning a PIV pipeline -- which automates pipeline choice for each image pair. A learned agent selects a configuration according to a chosen measurement objective from a library assembled for a specified speed budget. Across cross-correlation, classical optical-flow, and learning-based estimators, ART of PIV reduces average end-point error (AEPE) by 12.2-22.4 % relative to the best single estimator at matched speed, with gains extending to 1000-4000 image pair/s. In a simulated local velocity regulation task, it reduces mean normalized closed-loop cost by 13.24-13.59 percentage points relative to a tuned fixed estimator, with the same classical controller in both cases. In a tabletop water channel, we illustrate two ways to tune ART of PIV for a fluidic pinball of three rotating cylinders: selecting between complementary optical-flow tunings for accuracy, and selecting regions of interest with coherent rotation or shear using a common flow estimator. For the first variant, we train a DreamerV3 reinforcement learning controller directly in the physical world and demonstrate tracking of a prescribed flow field in the wake of a fluidic pinball.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Fluid Dynamics (physics.flu-dyn) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.41304 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:09:31.855Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).