Laboratory investigation of nominally two-dimensional anabatic flow on symmetric double slopes

We investigated the dynamics of highly turbulent thermally driven anabatic (upslope) flow on a physical model inside a large water tank using particle image velocimetry (PIV) and a thermocouple grid. The results showed that the flow exhibited pronounced variations in velocity and temperature and, importantly, could not be accurately modeled as a two-dimensional quasi-steady flow. Five significant findings are presented to underscore the three-dimensional nature of the flow. Namely, the B-shaped mean velocity profiles, B-shaped turbulent flux profiles, synthetic streaks that revealed particles flowing perpendicular to the laser sheet, average vorticity maps revealing helical structure splitting, and identified vortices shooting away from the boundary towards the apex plume. Collectively, these findings offer novel insights into the flow behavior patterns of thermally driven complex terrain flows, which influence local weather and microclimates and are responsible for scalar transport, e.g., pollution

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

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.Comment: In revie

Quasi-geostrophic jet-like flow with obstructions

Jet-like flows are ubiquitous in the atmosphere and oceans, and thus a thorough investigation of their behaviour in rotating systems is fundamental. Nevertheless, how they are affected by vegetation or, generally speaking, by obstructions is a crucial aspect which has been poorly investigated up to now. The aim of the present paper is to propose an analytical model developed for jet-like flows in the presence of both obstructions and the Coriolis force. In this investigation the jet-like flow is assumed homogeneous, turbulent and quasi-geostrophic, and with the same density as the surrounding fluid. Laws of momentum deficit, length scales, velocity scales and jet centreline are analytically deduced. These analytical solutions are compared with some experimental data obtained using the Coriolis rotating platform at LEGI-Grenoble (France), showing a good agreemen

Visual anemometry measurements of eight vegetation species

This repository consists of four files, namely, three data files and a README file: Records.csv NormalizationCoefficients.csv NormalizedRecords.csv README.txt Eight vegetation species were tested in the Center for Autonomous Systems and Technologies (CAST) facility at Caltech and exposed to wind speeds up to 15 m/s. Wind speeds were recorded using a two-component sonic anemometer (Campbell Scientific WindSonic4) sampling at 4 Hz. Vegetation motion was simultaneously recorded using a 1520 x 2704 CMOS camera (GoPro Hero7) sampling at 60 Hz. The vegetation tested were as follows: Vegetation 1      Muhlenbergia emersleyi  (Bullgrass) Vegetation 2      Cinnamomum camphora (Camphor tree) Vegetation 3      Prosopis alba thornless (Mesquite tree) Vegetation 4      Quercus agrifolia (Oak tree) Vegetation 5      Olea europaea (Olive tree) Vegetation 6      Melaleuca quinquenervia (Paperbark tree) Vegetation 7      Chinus mole (Pepper tree) Vegetation 8      Pinus radiata (Pine tree) The data in this repository were binned into 1 m/s bins +/-0.5 m/s in the range of 0-10 m/s. Replicates of each configuration were spaced two weeks apart to assess variability over multiple weeks and are referred to as Round 1 or Round 2 in the repository. The vegetation motion estimates were computed with PIVlab (Thielicke & Sonntag 2021). First, we manually selected 320 x 320 pixel bounding boxes of each vegetation to ensure the tunnel floor in the background is excluded from further processing. Then, we computed the displacement vector fields using two passes in the PIVlab algorithm: the first pass consisted of 64 x 64 pixel interrogation windows and the second pass consisted of 32 x 32 pixel interrogation windows, both with 50% overlap. To compare with pointwise wind speed records collected using the sonic anemometer, the vegetation displacement fields were spatially averaged. Temporal kinematics of the wind and vegetation speeds of each bin were fitted to a Weibull distribution separately. The maximum likelihood estimates and confidence intervals of the scale (C1) and shape (C2) factors of both wind and vegetation speeds are provided in this dataset. Each test set is defined to consist of all binned vegetation scale factors (canopy_C1) and their corresponding wind scale factors (wind_C1), separately for each round. The observed relationships between the vegetation displacement scale factors versus the wind scale factors resemble the shape of sigmoid logistic curves. Each test set was separately fitted to a sigmoid curve, canopy_C1=a/(1+exp(-(wind_C1-x0)/b)), where a, b, and x0 are the fitted coefficients computed using the Levenberg-Marquardt nonlinear least squares method. Here, a determines the scale of the higher wind asymptote, while x0 and b determine the center and range, respectively, of the region of highest correlation between vegetation speeds and wind speed. The fitted coefficients are later used to normalize each of the vegetation species to a universal curve that determines the relationship to wind speeds. The non-dimensional wind scale factor is computed using b and x0 from the sigmoid curve fitting: tilde{wind_C1}=(wind_C1*b)+x0. Similarly, the non-dimensional vegetation speed scale factor is computed using a from the sigmoid curve fitting: tilde{canopy_C1}=canopy_C1/a.   References Thielicke, W., & Sonntag, R. (2021). Particle Image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab. Journal of Open Research Software, 9. Ubiquity Press, Ltd. Retrieved from https://doi.org/10.5334%2Fjors.33