Particle tracking with continuous energy minimization for the study of segregation in bedload transport

Abstract

Bedload transport, the coarser component of sediment transport moving in contact with the bed in stream channels, has major implications for public safety, water resources, and environmental sustainability. Size segregation is largely responsible for our limited ability to predict sediment flux and river morphology, particularly in mountains where steep slopes drive an intense transport of a wide range of grain sizes. Two-size experiments were carried out in a dedicated 10% steep flume to study vertical segregation at the grain scale. Particle tracking was used to obtain trajectories of high concentration bedload mixtures of spherical particles, but it fails to correctly retrieve long trajectories due to strong grain–grain interactions. In this paper, we propose a new particle tracking algorithm using a global optimization scheme based on a Continuous Energy function and a specific iterative Minimization (CEM). For the purpose of evaluating this new algorithm named CEM-ST (available at https://gitlab.univ-st-etienne.fr/labhc-iscv/cem-st), we have designed two typical experimental reference sequences with corresponding full trajectory ground truths, made available to the community. Compared to online algorithms, which consider only previous time steps, this new CEM-ST algorithm is less sensitive to the quality of the detections and performs better both globally and in the details of the trajectories and the depth profiles of concentration, particle velocity and sediment transport rate. Application of CEM-ST has allowed us to gain a better insight into the influence of the fine particle rate on segregation, in particular unraveling the role of clusters in the bedload dynamics.