Visual anemometry: physics-informed inference of wind for renewable energy, urban sustainability, and environmental science

TL;DR

This paper reviews physics-informed methods for non-intrusive visual anemometry, which infers local wind conditions from environmental flow-structure interactions, aiding sustainability and environmental monitoring.

Contribution

It introduces emerging physics-based and machine learning approaches for visual anemometry, addressing challenges in measuring transparent microscale wind flows.

Findings

Physics-based visual anemometry techniques show promise.

Machine learning enhances flow inference accuracy.

Remaining obstacles include generalizability and environmental variability.

Abstract

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.