267 Advances in Fluvial Processes and Sediment Transport (Posters)

We encourage submissions on fluvial geomorphology, fluvial processes, and sediment transport including field studies, numerical modeling, physical modeling, and spatial analyses.

Wednesday, 22 October 2025
8:00 AM - 5:30 PM
HBGCC, Hall 1

Application of Settling Velocity Predictions to Atypical Sediment Morphologies and Densities

Paper number: 267-7
Program: Technical Sessions
Time: 8:00 AM – 8:00 AM

Authors

Abstract

The settling behavior of sediment grains reflects a dynamic balance between gravitational forces, which depend on grain size and density, and resistive forces shaped by fluid viscosity and grain-scale turbulence. These resistive effects are influenced by grain size, shape, surface roughness, and fluid properties. Existing settling velocity predictors accurately model the descent through water of typical natural grains, which are equant, moderately rounded sands and gravels with densities similar to quartz or feldspar. This study seeks to broaden the applicability of grain-settling predictions by evaluating how existing models perform when applied to grains that deviate substantially in shape and/or density from quartz sand. Ultimately, our goal is to develop a settling velocity predictor tailored to transport conditions within placer deposits, such as those responsible for the accumulation of gold flakes. To achieve this, we developed a method for estimating grain volume and shape by photographing individual grains under a microscope from three orthogonal views and measuring key dimensions. Grain mass is quantified using a high-precision microbalance. Settling velocity is determined by releasing grains into a water tank and tracking their descent photographically. The resulting dataset expands the body of data used to develop and validate grain-settling theory by contributing hundreds of new data points. We find that the equations of Dietrich (1982) consistently overpredict the settling velocity of gold grains by an average factor of approximately 10. A histogram of the measured and predicted velocity differences confirms this systematic bias. To improve predictive accuracy for heavy, irregularly-shaped grains, we propose a revision of the existing model.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-414613

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