Understanding the flow and transport of particles (e.g., snow, sand, pollens, etc.) in atmospheric environments is critical for applications related to wind energy, meteorology (e.g., snow settling), geomorphology (e.g., desert migration), oceanography (e.g., spray generation), agriculture (e.g., pollen dispersal), public health (e.g., airborne disease transmission), etc. These processes involve flows over a broad range of spatial and temporal scales and complex atmospheric phenomena which are impossible to be fully reproduced in the laboratory. Conventional field measurements (e.g. meteorological tower, LiDAR, Sodar and Radar) of these processes do not have sufficient resolutions to probe into their detailed underlying physics. To bridge this gap, with a team of flow physicists, computer scientists, and engineers, the proposal aims to develop a Grand-scale Atmospheric Imaging Apparatus (GAIA), a stand-alone and imaging-based field measuring system, able to quantify atmospheric flows and particle transport over large sample regions with unprecedented spatiotemporal resolution. Though collaboration with 11 university, national labs and industries across the globe, GAIA will enable fundamental and applied research across engineering, geoscience and computer science, and will support a number of existing educational programs involving underrepresented groups and minorities.
The goal of the project is to develop a Grand-scale Atmospheric Imaging Apparatus (GAIA), envisioned as a field instrument conducting particle image/tracking velocimetry (PIV/PTV) by exploiting particles (e.g., snow, sand, pollen, droplets, etc.) naturally present in the atmosphere to investigate both flow (using them as tracers) and the transport of the particles themselves depending on their inertial properties with respect to the flow. The development of GAIA innovates every single component of conventional PIV/PTV including both the hardware and processing software to address key challenges in conducting high-resolution flow imaging under harsh field conditions. Specifically, GAIA involves multi-mode and multi configuration Lego design and mechanical automation for the hardware and an integration of PIV/PTV concept with state-of-the-art machine learning multiview 3D scene reconstruction for data processing. Such innovation enables GAIA to conduct high-resolution imaging of flow and particle transport across a broad range of scales with sample volumes up to orders of magnitude larger than those of conventional PIV/PTVs. In addition, GAIA incorporates several unique sensors (e.g., digital inline holography) for in situ characterization of meteorological conditions and particle properties (e.g. shape, concentration, etc.) with unprecedented details. The GAIA will be tested under different field conditions in conjunction with cutting-edge 3D Doppler scanning LiDARs. Such integration enables the first-ever measurements of atmospheric flow and particle transport from sub-meter to kilometer scales, providing benchmark datasets not only for the fundamental study of atmospheric flow and particle transport, but also for learning-based motion reconstruction in computer science.
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.
