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Hairpin vortices are one of the most important vortical structures in turbulent flows. Extracting and characterizing hairpin vortices provides useful insight into many behaviors in turbulent flows. However, hairpin vortices have complex configurations and might be entangled with other vortices, making their extraction difficult. In this work, we introduce a framework to extract and separate hairpin vortices in shear-driven turbulent flows for their study. Our method first extracts general vortical regions with a region-growing strategy based on certain vortex criteria (e.g., $$\lambda_2$$) and then separates those vortices with the help of progressive extraction of ($$\lambda_2$$) iso-surfaces in a top-down fashion. This leads to a hierarchical tree representing the spatial proximity and merging relation of vortices. After separating individual vortices, their shape and orientation information is extracted. Candidate hairpin vortices are identified based on their shape and orientation information as well as their physical characteristics. An interactive visualization system is developed to aid the exploration, classification, and analysis of hairpin vortices based on their geometric and physical attributes. We also present additional use cases of the proposed system for the analysis and study of general vortices in other types of flows. 

Abstract Drought-induced productivity reductions and tree mortality have been increasing in recent decades in forests around the globe. Developing adaptation strategies hinges on an adequate understanding of the mechanisms governing the drought vulnerability of forest stands. Prescribed reduction in stand density has been used as a management tool to reduce water stress and wildfire risk, but the processes that modulate fine-scale variations in plant water supply and water demand are largely missing in ecosystem models. We used an ecohydrological model that couples plant hydraulics with groundwater hydrology to examine how within-stand variations in tree spatial arrangements and topography might mitigate forest vulnerability to drought at individual-tree and stand scales. Our results demonstrated thinning generally ameliorated plant hydraulic stress and improved carbon and water fluxes of the remaining trees, although the effectiveness varied by climate and topography. Variable thinning that adjusted thinning intensity based on topography-mediated water availability achieved higher stand productivity and lower mortality risk, compared to evenly-spaced thinning at comparable intensities. The results from numerical experiments provided mechanistic evidence that topography mediates the effectiveness of thinning and highlighted the need for an explicit consideration of within-stand heterogeneity in trees and abiotic environments when designing forest thinning to mitigate drought impacts. 

Effects of helical-shaped blades on the flow characteristics and power production of finite-length wind farms composed of vertical-axis wind turbines (VAWTs) are studied numerically using large-eddy simulation (LES). Two helical-bladed VAWTs (with opposite blade twist angles) are studied against one straight-bladed VAWT in different array configurations with coarse, intermediate, and tight spacings. Statistical analysis of the LES data shows that the helical-bladed VAWTs can improve the mean power production in the fully developed region of the array by about 4.94%–7.33% compared with the corresponding straight-bladed VAWT cases. The helical-bladed VAWTs also cover the azimuth angle more smoothly during the rotation, resulting in about 47.6%–60.1% reduction in the temporal fluctuation of the VAWT power output. Using the helical-bladed VAWTs also reduces the fatigue load on the structure by significantly reducing the spanwise bending moment (relative to the bottom base), which may improve the longevity of the VAWT system to reduce the long-term maintenance cost. 

Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.