Cross-plane stereo-PIV measurements in a refractive-index-matched environment of flow associated with barchan dunes immersed in a turbulent boundary layer

Barchan dunes are crescent-shaped bedforms that form in aeolian (i.e., wind-driven) environments (including both Earth and other planets, such as Mars) as well as subaqueous environments. Under the forcing of the aloft turbulent boundary layer, they migrate downstream at a rate inversely proportional to their size, which results in complex interactions between neighboring dunes of disparate scales. In particular, it has been observed that dunes will interact at a distance, causing changes in morphology without contacting each other, which is thought to be driven by the way dunes modify the local flow field Bristow et al. (2018); Assis and Franklin (2020). In this study, the coherent structures formed in the wakes of barchan dunes are investigated using measurements of the flow over fixed-bed (i.e., solid) barchan models, both in the wake of an isolated barchan and the interdune region between interacting barchans (Fig. 1(a)). Furthermore, the interactions between the flow structures shed by the dunes and the structures in the incoming boundary layer are analyzed.

The Occurrences and Formation of Ladderback Ripples on Barchans Dunes of Najaf Dunes Field, Iraq, in Aeolian Environments

This paper concerns the study of ripples that occur on the windward of Barchan dunes from the dunes field of Najaf governorate, Iraq. These dunes consist mainly of sand sediments with variable sizes, including medium, fine, and very fine sands. Quartz represents the major light mineral in the Najaf Dunes sand. The prevailing wind direction in the study area is NW-SE. The major ripple crest series of every pattern are oriented perpendicular to the NW-SE wind direction, whereas imbricated ripple groups within the troughs of the preexisting ripples are created by the WSW-ENE wind trend. These ripples tend to be formed by shortened ripples that occupy the troughs of the prolonged series. All crests of the ladderback ripples are oriented at right angles to asymmetry ripples. The ladderback ripples were noticed from fine to very fine- grained sediments, which consist mainly of quartz. The wavelength of the ladderback ripples ranges from 2 – 4 cm, while they are 0.1 – 0.2 cm in height. The occurrence of ladderback ripples within an aeolian environment indicates a variety of wind directions, which influenced the arrangements of the crest ripples.

Mapping Surface Winds on Mars from the Global Distribution of Barchan Dunes Employing an Instance Segmentation Neural Network

<p>The surface of Mars is riddled with dunes that form by accumulating sand particles that are carried by the wind. Since dune geometry and orientation adjust in response to prevailing wind conditions, the morphometrics of dunes can reveal information about the winds that formed them. <br><br>Previous studies inferred the prevailing local wind direction from the orientation of dunes by manually analyzing spacecraft imagery. However, building a global map remained challenging, as manual detection of individual dunes over the entire Martian surface is impractical. Here, we employ Mask R-CNN, a state-of-the-art instance segmentation neural network, to detect and analyze isolated barchan dunes on a global scale.<br><br>We prepared a training dataset by extracting Mars Context Camera (CTX) scenes of dune fields from a global CTX mosaic, as identified in the global dune-fields catalog. Images were cropped and standardized to a resolution of 832x832 pixels, and labeled using Labelbox’s online instance segmentation platform. Image augmentation and weight decay were employed to prevent overfitting during training. By inspecting 100 sample images from the validation database, we find that the network correctly identified ~86% of the isolated dunes, falsely identifying one feature as a barchan dune in a single image.</p><p>After dune outlines are detected, they are automatically analyzed to extract the dominant-wind and net sand-flux directions using traditional computer vision techniques. We expect our future surface-wind dataset to serve as a constraint for atmospheric global circulation models to help predict weather events for upcoming in situ mission as well as shed new light on the recent climate history of Mars.</p>