Author(s): Xin Lu; Bruce W. Melville; Asaad Y. Shamseldin; Lu Wang
Linked Author(s): Lu Wang, Bruce W. Melville
Keywords: Smart pebble; Drag and lift forces; Relative submergence; Flume experiments; Sediment transport
Abstract: Low-gradient rivers have higher sediment transport rates and lower flow resistance than expected when compared to steep mountain streams, and often these differences are ascribed to increased near-bed flow velocities and stresses arising from form drag on channel morphology and immobile large sediment particles. However, there has been limited direct investigation into drag and lift forces exerted on bed sediment with varying densities under different water depth (H) conditions, which ultimately control sediment transport rates and grain-scale flow resistance. To explore the entrainment mechanism of large sediment particles under these flow conditions, we conducted a set of flume experiments using the Smart Pebble (SP) over a range of densities (1.058~1.395 g/cm3) and relative submergence (0.8≤H/D≤4). The SP, with a diameter (D) of 60 mm, is equipped with inertial measurement units (IMUs) such as accelerometers and gyroscopes, which can measure instantaneous dynamics like acceleration and angular velocity. All experiments had the same flume slope, fixed bed layout, and pebble protrusion height. Drag and lift have no correlation with relative submergence in fully submerged conditions (H/D≥1), and they are lesser than those under partially submerged conditions (H/D<1). Additionally, the greater the SP density, the greater the drag and lift force. When H/D≥1, we find that the drag coefficient (CD) shows no dependency on relative submergence, consistent with previous research findings, and increases with SP density. In contrast, the lift coefficient (CL) has no relationship with H/D and SP density; when H/D<1, the CD and CL for SP with different densities are constant, approximately 17.73 and 33.69, respectively. These findings deepen our understanding of the entrainment mechanism of pebbles and introduce novel perspectives on data acquisition (direct access to particle dynamics during movement) and subsequent analysis.
Year: 2024