视觉风速测量法：面向可再生能源、城市可持续发展与环境科学的物理 informed 风场推断
Visual anemometry: physics-informed inference of wind for renewable energy, urban sustainability, and environmental science
AI总结：
该综述针对风的光学透明性导致的微尺度风场测量难题，介绍了基于流-结构相互作用的视觉风速测量技术，涵盖物理第一性原理与机器学习方法，并探讨了其通用化面临的障碍。
摘要：
米级分辨率的大气流动精确测量对众多可持续发展应用至关重要，包括风电场与光伏电站的优化设计、安全高效的城市空中交通、野火与空气污染扩散等环境现象的监测，以及天气和气候模型的数据同化。由于风具有光学透明性，相关微尺度风场的测量本身就存在挑战。本综述探讨了利用物理学以非侵入式方式“观测”环境流动的新方法，即无需将测量仪器直接放置在目标流动中。具体而言，尽管风本身是透明的，但可以通过环境中受风力作用的物体的运动来直观观测其影响——摇摆的树木和飘动的旗帜就是常见例子。我们阐述了实现视觉风速测量（即基于观测到的流-结构相互作用物理学原理定量推断局部风况的任务）的新兴研究成果，将介绍基于第一性原理物理学以及数据驱动机器学习方法的各类途径，并讨论实现完全通用的视觉风速测量仍存在的障碍。
英文摘要：
Accurate measurements of atmospheric flows at meter-scale resolution are essential for a broad range of sustainability applications, including optimal design of wind and solar farms, safe and efficient urban air mobility, monitoring of environmental phenomena such as wildfires and air pollution dispersal, and data assimilation into weather and climate models. Measurement of the relevant microscale wind flows is inherently challenged by the optical transparency of the wind. This review explores new ways in which physics can be leveraged to "see" environmental flows non-intrusively, that is, without the need to place measurement instruments directly in the flows of interest. Specifically, while the wind itself is transparent, its effect can be visually observed in the motion of objects embedded in the environment and subjected to wind -- swaying trees and flapping flags are commonly encountered examples. We describe emerging efforts to accomplish visual anemometry, the task of quantitatively inferring local wind conditions based on the physics of observed flow-structure interactions. Approaches based on first-principles physics as well as data-driven, machine learning methods will be described, and remaining obstacles to fully generalizable visual anemometry will be discussed.
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