Sand particle kinematics under different transport conditions

Dataset

Description

Abstract
The kinematics of sediment particles is investigated by non-intrusive imaging to provide a stochastic description of bedload transport in conditions near the threshold of motion. In particular, we focus on the cyclic transition between motion and rest regimes to quantify the statistical properties of the particles waiting times, inferred to be responsible for anomalous diffusion, and so far elusive. The probability distributions of the particle step time and step length, streamwise and spanwise velocity, acceleration, and waiting time are quantified experimentally. Results suggest that variables associated with the particle step motion are thin-tailed distributed while the waiting times exhibit a power law distribution. The experimental results shown here, under five different transport conditions, describe grain scale kinematics and dynamics at increasing shear stress and represent a benchmark dataset for the stochastic modeling of bedload transport.

Description
The sediment particles used in all experiments are from a coarse quartz sand mixture with uniform gradation, and an average diameter D50 = 1.1mm (D20 = 0.9mm, D80 = 1.4mm). The erodible bed was flattened before starting each experiment. A slightly submerged camera imaging at 120 frames per second over a 6.3 -7.3cm (stream wise) × 4.7-5.5 cm (cross-stream) bed surface domain with 640×480 pixel resolution provided the data for tracking particle motion. The field of view was illuminated by a downstream-located halogen lamps equipped with a lighting reflector paper to diffuse the light and to reduce background image fluctuations due to free surface undulation. 5 set of multiple videos were acquired under different transport conditions S1-S5, characterized by a shear velocity : 0.018252, 0.0216, 0.02268, 0.02376, 0.02592 m/s

Funding information
Sponsorship: University of Minnesota
Date made available2019
PublisherData Repository for the University of Minnesota
Date of data productionJan 1 2017 - Dec 30 2017

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