Dispersion of Oil Droplets in Rivers
Publication: Journal of Hydraulic Engineering
Volume 147, Issue 3
Abstract
The dispersion of oil droplets in rivers was numerically investigated for uniform flow in a hypothetical wide river with a depth of . The river hydrodynamics profile was used in conjunction with the VDROP model to produce the oil droplet size distribution (DSD), whereas the NEMO3D model was used to track the movement of the oil droplets. Results suggest that the gradient of eddy diffusivity significantly affected the upward-normal (i.e., quasi-vertical) transport of the droplets, and caused them to mix rapidly through the depth. We also found that an increase in buoyancy resulted in a decrease in the streamwise variance and spreading coefficient. Oil droplets broke up due to the relatively large energy dissipation rates in the river at approximately below the surface and deeper. The droplet breakup varies DSD in the river water column, which may subsequently affect other chemo-physical processes (e.g., oil-particle aggregation). The breakup efficiency is affected by a system-dependent parameter , which reflects the uncertainty of a system. The steady-state DSD was bimodal for the case , whereas it was unimodal for larger values (i.e., and 0.25), respectively. More small-sized droplets were generated and persisted in the deep-water column with larger values. The droplet breakup also enhanced the streamwise spreading of the plume. The effect of droplet entrainment on oil dispersion was studied by assuming constant entrainment probabilities of surface oil droplets. The oil DSD varied with different droplet entrainment probabilities, and the number of oil droplets generated in the water column decreased significantly with a decrease in the entrainment probability of the oil droplets.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (numerical results).
Acknowledgments
This research was supported by a grant from the Multi-Partner Research Initiative under Grant No. MECTS-#3939073-v1-OFSCP. However, no official endorsement should be implied by these entities. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation (NSF) Grant No. TG-BCS190002. Specifically, we used the Bridges computer cluster, which is an NSF-funded system at the Pittsburgh Supercomputing Center (PSC).
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Journal of Hydraulic Engineering
Volume 147 • Issue 3 • March 2021
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© 2021 American Society of Civil Engineers.
History
Received: Sep 25, 2019
Accepted: Oct 5, 2020
Published online: Jan 11, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 11, 2021
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