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Fangda Cui, Ph.D.
  • Home
  • About Me
  • Projects
    • P1. Marine Oil Spill Detection and Monitoring
    • P2. Underwater Object Detection for Marine Environmental Monitoring
    • P3. Computer Vision System for Marine 3D Object Detection
    • P4. Computer Vision System for Marine 2D Object Detection
    • P5. Obstacle Detection and Collision Prevention for Marine Vessels
    • P6. Experimental Measuremnt and Numerical Simulation of Wind-wave Interacti
    • P7. Large-eddy Simulation of Deep-water Breaking Waves
    • P8. Large-scale Oil Spill Modeling and Simulation
    • CP1. Modeling and Simulation of Oil Dispersion under Breaking Waves
    • CP2. Experimental Measurement and Modeling of Oil Droplet Dispersion
    • CP3. Transport and Fate of Virus-laden Particles in a Supermarket
    • CP4. Modeling the Transport and Formation of OPAs in Riverine Environments
    • CP5. Constitutive Modeling and Numerical Simulation of Shape Memory Polymer
    • CP6. A Finite Element Method of Light-activated Polymeric Materials
    • CP7. Advesarial Attack and Defensive Strategies for NIDS
  • Publications
  • Services
  • Gallery
  • Contact
Fangda Cui, Ph.D.
  • Home
  • About Me
  • Projects
    • P1. Marine Oil Spill Detection and Monitoring
    • P2. Underwater Object Detection for Marine Environmental Monitoring
    • P3. Computer Vision System for Marine 3D Object Detection
    • P4. Computer Vision System for Marine 2D Object Detection
    • P5. Obstacle Detection and Collision Prevention for Marine Vessels
    • P6. Experimental Measuremnt and Numerical Simulation of Wind-wave Interacti
    • P7. Large-eddy Simulation of Deep-water Breaking Waves
    • P8. Large-scale Oil Spill Modeling and Simulation
    • CP1. Modeling and Simulation of Oil Dispersion under Breaking Waves
    • CP2. Experimental Measurement and Modeling of Oil Droplet Dispersion
    • CP3. Transport and Fate of Virus-laden Particles in a Supermarket
    • CP4. Modeling the Transport and Formation of OPAs in Riverine Environments
    • CP5. Constitutive Modeling and Numerical Simulation of Shape Memory Polymer
    • CP6. A Finite Element Method of Light-activated Polymeric Materials
    • CP7. Advesarial Attack and Defensive Strategies for NIDS
  • Publications
  • Services
  • Gallery
  • Contact
  • More
    • Home
    • About Me
    • Projects
      • P1. Marine Oil Spill Detection and Monitoring
      • P2. Underwater Object Detection for Marine Environmental Monitoring
      • P3. Computer Vision System for Marine 3D Object Detection
      • P4. Computer Vision System for Marine 2D Object Detection
      • P5. Obstacle Detection and Collision Prevention for Marine Vessels
      • P6. Experimental Measuremnt and Numerical Simulation of Wind-wave Interacti
      • P7. Large-eddy Simulation of Deep-water Breaking Waves
      • P8. Large-scale Oil Spill Modeling and Simulation
      • CP1. Modeling and Simulation of Oil Dispersion under Breaking Waves
      • CP2. Experimental Measurement and Modeling of Oil Droplet Dispersion
      • CP3. Transport and Fate of Virus-laden Particles in a Supermarket
      • CP4. Modeling the Transport and Formation of OPAs in Riverine Environments
      • CP5. Constitutive Modeling and Numerical Simulation of Shape Memory Polymer
      • CP6. A Finite Element Method of Light-activated Polymeric Materials
      • CP7. Advesarial Attack and Defensive Strategies for NIDS
    • Publications
    • Services
    • Gallery
    • Contact

Modeling the Transport and Formation of OPAs in Riverine Environments 

A numerical framework was developed to study oil-particle aggregate (OPA) formation by incorporating NEMO3D code andthe A-DROP model into an open-source platform, OpenFOAM. The developed framework was then used to study oil transport and OPAformation in a two-dimensional (2D) hypothetical river at a depth of 3.0 m. The river’s hydrodynamic profile was used in conjunction withthe A-DROP model to simulate OPA formation, whereas the NEMO3D model was used to track the movement of the oil droplets and OPAs. Results suggest that an increase in buoyancy results in a decrease in the streamwise variance and spreading coefficient. The small (i.e., 50 μm) droplets became entrained in the deep water column at high-energy dissipation rates, which enhanced OPA formation. The large (i.e., 200 μm) droplets aggregated much more rapidly than the small ones in the same turbulence environment owing to differentialsedimentation. In general, OPA formation in the upper rivers was dominated by collisions caused by differential sedimentation, whilein the deep water column, collision caused by turbulence shear plays a more critical role. The aggregation rates of the formed OPAs wereless than 60% within a short period (20 min) in a river with relatively mild turbulence (ε < 5 × 10−4 W/kg). DOI: 10.1061/(ASCE)EE.1943-7870.0001875. © 2021 American Society of Civil Engineers.

Published papers for details:

Cui, F., Daskiran, C., Lee, K., Boufadel, M.C., 2021. Transport and Formation of OPAs in Rivers. Journal of Environmental Engineering 147 (5), 04021012

Cui, F., Behzad, H., Geng, X., Zhao, L., Lee, K., Boufadel, M.C., 2021. Dispersion of oil droplets in rivers. Journal of Hydraulic Engineering 147 (3), 04021004

Figure 1. Schematic illustration of oil transport and OPA formation in rivers.

Figure 2. Spatial distribution of 200-μm oil droplets at various times: (a) 250 s; (b) 500 s; (c) 750 s; (d) 1,000 s; (e) 1,250 s; and (f) 1,500 s. Note that theoil stayed compact near the river surface because of the large buoyancy and was transported forward faster but spread less than 50-μm oil droplets.

Figure 3. Spatial distribution of 200-μm oil droplets at various times with OPA formation (note contour is in logarithmic scale): (a) 75 s; (b) 150 s;(c) 225 s; (d) 300 s; (e) 375 s; and (f) 450 s. The dotted-dashed lines overlapping on the oil concentration contours are the isolines of the numberconcentration of OPAs formed by the interaction between 200-μm oil droplets and particles.

Figure 4. Spatial distribution of OPAs formed by 200-μm oil droplets at various times (note contour is in logarithmic scale): (a) 75 s; (b) 150 s; (c) 225 s; (d) 300 s; (e) 375 s; and (f) 450 s. The black dashed lines with percentage values indicate the boundary of the OPAs with different aggregation levels.

Figure 5. Time series of percentage of unaggregated oil for 50- and 200-μm droplets.

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