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USGS · ofr20181010

Laboratory observations of artificial sand and oil agglomerates

Abstract

Sand and oil agglomerates (SOAs) form when weathered oil reaches the surf zone and combines with suspended sediments. The presence of large SOAs in the form of thick mats (up to 10 centimeters [cm] in height and up to 10 square meters [m 2 ] in area) and smaller SOAs, sometimes referred to as surface residual balls (SRBs), may lead to the re-oiling of beaches previously affected by an oil spill. A limited number of numerical modeling and field studies exist on the transport and dynamics of centimeter-scale SOAs and their interaction with the sea floor. Numerical models used to study SOAs have relied on shear-stress formulations to predict incipient motion. However, uncertainty exists as to the accuracy of applying these formulations, originally developed for sand grains in a uniformly sorted sediment bed, to larger, nonspherical SOAs. In the current effort, artificial sand and oil agglomerates (aSOAs) created with the size, density, and shape characteristics of SOAs were studied in a small-oscillatory flow tunnel. These experiments expanded the available data on SOA motion and interaction with the sea floor and were used to examine the applicability of shear-stress formulations to predict SOA mobility. Data collected during these two sets of experiments, including photographs, video, and flow velocity, are presented in this report, along with an analysis of shear-stress-based formulations for incipient motion. The results showed that shear-stress thresholds for typical quartz sand predicted the incipient motion of aSOAs with 0.5–1.0-cm diameters, but were inaccurate for aSOAs with larger diameters (>2.5 cm). This finding implies that modified parameterizations of incipient motion may be necessary under certain combinations of aSOA characteristics and environmental conditions.

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BibTeXRIS

Robert L. Jenkins, P. Soupy Dalyander, Allison Penko, Joseph W. Long. 2018-04-27. Laboratory observations of artificial sand and oil agglomerates. https://doi.org/10.3133/ofr20181010

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