This research aims to advance current knowledge on cluster formation and evolution by tackling some of the aspects associated with cluster microtopography and the effects of clusters on bedload transport. The specific objectives of the study are (1) to identify the bed shear stress range in which clusters form and disintegrate, (2) to quantitatively describe the spacing characteristics and orientation of clusters with respect to flow characteristics, (3) to quantify the effects clusters have on the mean bedload rate, and (4) to assess the effects of clusters on the pulsating nature of bedload. In order to meet the objectives of this study, two main experimental scenarios, namely, Test Series A and B (20 experiments overall) are considered in a laboratory flume under well-controlled conditions. Series A tests are performed to address objectives (1) and (2) while Series B is designed to meet objectives (3) and (4). Results show that cluster microforms develop in uniform sediment at 1.25 to 2 times the Shields parameter of an individual particle and start disintegrating at about 2.25 times the Shields parameter. It is found that during an unsteady flow event, effects of clusters on bedload transport rate can be classified in three different phases: a sink phase where clusters absorb incoming sediment, a neutral phase where clusters do not affect bedload, and a source phase where clusters release particles. Clusters also increase the magnitude of the fluctuations in bedload transport rate, showing that clusters amplify the unsteady nature of bedload transport. A fourth-order autoregressive, autoregressive integrated moving average model is employed to describe the time series of bedload and provide a predictive formula for predicting bedload at different periods. Finally, a change-point analysis enhanced with a binary segmentation procedure is performed to identify the abrupt changes in the bedload statistic characteristics due to the effects of clusters and detect the different phases in bedload time series using probability theory. The analysis verifies the experimental findings that three phases are detected in the bedload rate time series structure, namely, sink, neutral, and source.

A field study was conducted to examine the turbulent properties of the flow around a naturally formed cluster bedform. This was carried out on a mountain river using a 3D acoustic Doppler velocimeter (ADV). Concurrent with this objective, the ability of the ADV to make measurements in a shallow flow over a cobble bed ( ho ∕ d84 =1.75) was examined. ADV measurements in natural clear-water shallow flows around obstacles are inherently difficult to obtain due to (1) signal interference between the acoustic reflections from the boundary and the ADV sample volume; (2) regions of high turbulent intensity, such as in local detached shear layers; and (3) low concentrations of suspended particles passing through the ADV sampling volume. These processes result in velocity time series that contain a significant amount of spikes, lower-than-average signal-to-noise ratios, and lower than average correlation values. A filtering process that optimized the removal of bad points while retaining a sufficient number of poin...

: A simple explicit model for calculating the settling velocity of mud flocs in both the viscous and inertial settling range is presented. The model is based on a nondimensional settling velocity and floc diameter, and the basic structure of the model follows that of Ferguson and Church (2004) with modifications for the fractal structure of flocculated sediment. The explicit form of the equation simplifies settling-velocity calculations and makes it easier to code into larger computational models of river plume sedimentation and morphodynamics. Input parameters needed for calculating settling velocity include: the floc size, the size of the primary particles, the fractal dimension of the floc, the specific gravity of the sediment, and values for two coefficients which incorporate effects due to floc shape, permeability, and flow separation on drag. The behavior of the model using a constant and variable fractal dimension over a range of parameter space is presented. The model is also compared to historic settling-velocity data from in situ measurements and laboratory settling-column data. For much of the field and laboratory data examined, floc settling remained viscous and is best described with a constant fractal dimension. Flocs with fractal dimensions equal to and greater than 2.5 are interpreted as impermeable aggregates, and calculated drag coefficients are larger than those of a smooth solid sphere of equivalent-volumetric diameter. It is suggested that the increased drag is due to the increased surface area of a floc in contact with the fluid relative to a smooth sphere of equivalent diameter. For flocs with fractal dimensions less than 2, calculated drag coefficients are on the order of those for a solid particle of equivalent diameter, even though it is likely that flocs have a larger relative surface area; this phenomenon is interpreted as being the outcome of flow passing through the more permeable flocs as they settle. Estimates of settling-velocity model coefficients are given for these two broad sets of floc settling behavior, and a recommended methodology for using measured settling velocity data to calibrate the model is presented.