Unmanned aerial vehicles are increasingly used to study atmospheric structure and dynamics. While much emphasis has been on the development of fixed-wing unmanned aircraft for atmospheric investigations, the use of multirotor aircraft is relatively unexplored, especially for capturing atmospheric winds. The purpose of this article is to demonstrate the efficacy of estimating wind speed and direction with 1) a direct approach using a sonic anemometer mounted on top of a hexacopter and 2) an indirect approach using attitude data from a quadcopter. The data are collected by the multirotor aircraft hovering 10 m above ground adjacent to one or more sonic anemometers. Wind speed and direction show good agreement with sonic anemometer measurements in the initial experiments. Typical errors in wind speed and direction are smaller than 0.5 and 30°, respectively. Multirotor aircraft provide a promising alternative to traditional platforms for vertical profiling in the atmospheric boundary layer, especially in conditions where a tethered balloon system is typically deployed.

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The topology of natural fracture networks is inherently linked to the structure of the fluid velocity field and transport therein. Here we study the impact of network density on flow and transport behaviors. We stochastically generate fracture networks of varying density and simulate flow and transport with a discrete fracture network model, which fully resolves network topology at the fracture scale. We study conservative solute trajectories with Lagrangian particle tracking and find that as fracture density decreases, solute channelization to large local fractures increases, thereby reducing plume spreading. Furthermore, in sparse networks mean particle travel distance increases and local network features, such as velocity zones where flow is counter to the primary pressure gradient, become increasingly important for transport. As the network density increases, network statistics homogenize and such local features have a reduced impact. We quantify local topological influence on transport behavior with an effective tortuosity parameter, which measures the ratio of total advective distance to linear distance at the fracture scale; large tortuosity values are correlated to slow-velocity regions. These large tortuosity, slow-velocity regions delay downstream transport and enhance tailing on particle breakthrough curves. Finally, we predict transport with an upscaled, Bernoulli spatial Markov random walk model and parameterize local topological influences with a novel tortuosity parameter. Bernoulli model predictions improve when sampling from a tortuosity distribution, as opposed to a fixed value as has previously been done, suggesting that local network topological features must be carefully considered in upscaled modeling efforts of fracture network systems. and topology, and the corresponding flow field. The flow field within an individual fracture is typically highly correlated, commonly causing solute velocity to display persistent, low variability behavior over the in-fracture scale; consequently, the greatest Lagrangian accelerations occur at fracture intersections . As the fracture density increases, solute encounters more intersections on average, and the velocity correlation scale decreases. Furthermore, strong preferential flow paths form within interconnected networks of large fractures and channel a significant portion of mass, enabling solute to persist at high velocities for distances greater than the single fracture scale Sherman et al., 2019). This channelization becomes enhanced in sparse networks, where particles encounter fewer intersections, enabling them to persist on single fractures for longer distances. Resolving all these intranetwork features in 3-D DFN models is still computationally costly, and so upscaled modeling approaches, which account for network variability through effective parameter schemes, while maintaining a parsimonious framework, present an attractive alternative. However, how to properly parameterize network properties, such as velocity correlation and geometry, and inco...

We characterize the influence of different intersection mixing rules for particle tracking simulations on transport properties through three-dimensional discrete fracture networks. It is too computationally burdensome to explicitly resolve all fluid dynamics within a large three-dimensional fracture network. In discrete fracture network (DFN) models, mass transport at fracture intersections is modeled as a sub-grid scale process based on a local Péclet number. The two most common mass transfer mixing rules are 1) complete mixing, where diffusion dominates mass transfer, and 2) streamline routing, where mass follows pathlines through an intersection. Although, it is accepted that mixing rules impact local mass transfer through single intersections, the effect of the mixing rule on transport at the fracture network scale is still unresolved. Through the use of explicit particle tracking simulations, we study transport through a quasi-two-dimensional lattice network and a three dimensional network whose fracture radii follow a truncated power law distribution. We find that the impact of the mixing rule is a function of the initial particle injection condition, the heterogeneity of the velocity field, and the geometry of the network. Furthermore, our particle tracking simulations show that the mixing rule can particularly impact concentrations on secondary flow pathways. We relate these local differences in concentration to reactive transport and show that streamline routing increases the average mixing rate in DFN simulations.