ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control
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.
粒子图像测速（PIV）通过示踪粒子的图像来测量流体运动，但其准确性取决于整个估计流程中的选择，包括图像预处理、流场估计和后处理。这些选择通常留给专家进行调整，且当条件发生变化时，固定的配置可能会变得不再准确。
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.
当 PIV 为实时控制提供反馈时，这一点尤为重要，因为此时测量必须既准确又快速，且流场条件多变。我们引入了 ART of PIV（Agentic Real-Time 的缩写，同时也致敬了调整 PIV 流程的艺术），它能为每一对图像自动选择处理流程。
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.
一个经过学习的智能体根据选定的测量目标，从为特定速度预算而构建的库中选择配置。在互相关、经典光流法和基于学习的估计器中，ART of PIV 在匹配速度下，相较于最佳单一估计器，将平均端点误差（AEPE）降低了 12.2-22.4%，且增益范围可达每秒 1000-4000 对图像。
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.
在模拟的局部速度调节任务中，与经过调整的固定估计器相比，在均使用相同经典控制器的情况下，它将平均归一化闭环成本降低了 13.24-13.59 个百分点。在桌面水槽实验中，我们展示了两种为三个旋转圆柱体组成的“流体弹球”调整 ART of PIV 的方法：在互补的光流调整方案中进行选择以提高精度，以及使用通用流场估计器选择具有相干旋转或剪切的感兴趣区域。