Seeing the Wind: Leveraging flow-structure interactions for visual anemometry

准确测量风速对于许多工程应用至关重要，包括风能、天气预报、无人机导航以及减轻空气污染、野火和空气传播病原体等危险。今天使用的大多数测风设备都是固定在太空中的，部署成本很高。然而，建筑环境已经自然地配备了各种各样的结构，这些结构会随着风而移动，从摇摆的树木到飘动的旗帜。该项目的主要目的是开发基于物理的理解这些流动结构的相互作用，并将这些知识与机器学习技术联合收割机相结合，以实现前所未有的风力测量能力。该项目将包括在一个大型风洞中对暴露于受控风条件下的植被进行实验室测量，以及对自然发生的风进行实地测量。K-12的参与将通过强调这项研究的及时应用来促进，特别是消防和污染监测，这两者都是学生居住的南加州面临的紧迫挑战。最初的概念验证已经证明了使用视觉测量植被的流结构相互作用的可行性，以解决风速使用神经网络。该项目的目标是通过将基于物理的对流-结构交互的理解纳入网络架构，实现超出初始训练数据集的泛化。第一个目标是确定哪些物理特性的流-结构相互作用可以提取的模型，是必要的准确的风速预测。然后，更广泛的训练集和基于物理的约束将使风速推断不用于训练模型的植被类别。一个正式的事后分析的神经网络将进一步阐明显着的流动物理。该项目可以通过开发和后续分析基于物理学的机器学习方法来提高对流动-结构相互作用的物理理解。从数据驱动方法中获得的物理见解将与纯粹基于物理的第一原理方法一起考虑沿着，以推广模型并更好地理解其局限性。此外，由于基于物理学的机器学习在基础研究中具有广泛的意义，因此该项目中的视觉风速测量方法可以为其他研究问题的类似努力提供模板。该项目的一个主要成果，除了有关流体动力学的新知识和物理学的机器学习的发展，将是创建一个强大的，定量的视觉风速测量技术。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Accurate measurements of wind speeds are crucial for many engineering applications, including wind energy, weather forecasting, drone navigation, and mitigation of dangers such as air pollution, wildfires, and airborne pathogens. Most wind-measuring devices used today are fixed in space and have high deployment costs. However, the built environment is already naturally instrumented with a variety of structures that move in response to the wind, from swaying trees to flapping flags. The principal aim of this project is to develop a physics-based understanding of those flow-structure interactions and to combine that knowledge with machine learning techniques to achieve unprecedented wind measurement capabilities. The project will incorporate laboratory measurements of vegetation exposed to controlled wind conditions in a large wind tunnel, and also field measurements in naturally occurring winds. K-12 engagement will be facilitated by emphasizing the timely applications of this research, especially firefighting and pollution monitoring, both of which are pressing challenges in southern California where the students live. An initial proof-of-concept has demonstrated the feasibility of using visual measurements of flow-structure interactions of vegetation to resolve wind speeds using a neural network. The goal of this project is to enable generalization beyond the initial training data set by incorporating a physics-based understanding of flow-structure interactions into the network architecture. The first objective is to determine which physical properties of the flow-structure interaction can be extracted by the model and are necessary for accurate wind speed predictions. Then, a broader training set and physics-based constraints will enable wind speed inference from classes of vegetation not used to train the model. A formal post hoc analysis of the neural network will further elucidate the salient flow physics. This project can improve the physical understanding of flow-structure interactions through development and subsequent analysis of the physics-based, machine learning approach to visual anemometry. The physical insights that will be gained from the data-driven approach will be considered along with a purely physics-based, first-principles approach to generalize the model and better understand its limitations. Moreover, because physics-informed machine learning is of broad interest in basic research, the approach to visual anemometry in this project can provide a template for similar efforts toward other research questions. A main outcome of this project, in addition to new knowledge regarding fluid dynamics and development of physics-informed machine learning, will be creation of a robust, quantitative visual anemometry technique.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.