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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
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  • 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

Experimental Measurement and Numerical Modeling of Oil Droplet Dispersion under a Plunging Breaker  

Knowledge of the droplet size distribution (DSD) of spilled oil is essential for the accurate prediction of oil transport, dissolution, and biodegradation. Breaking waves play important roles in oil droplet formation in oceanic environments. To understand the effects of breaking waves on oil DSD, oil spill experiments were designed and performed in a large-scale wave tank. A plunging breaker with a height of about 0.4 m was produced using the dispersive focusing method within the tank. Oil placed within the breaker resulted in a DSD that was measured using a shadowgraph camera and found to fit a Gaussian distribution N(µ = 1.2 mm, σ2 = 0.29 mm2). For droplets smaller than 1500 µm, the number-based DSD matched the DS1988 correlation, which gives N(d) ~ d−2.3, but this was N(d) ~ d−9.7 for droplets larger than 1500 µm. An order of magnitude investigation revealed that a Gaussian volume-based DSD results in a number-based DSD that may be approximated by d−b (with b ≈ 2) for small diameters (relative to the mean), which explains the occurrence of the DS1988 correlation. With the measured wave hydrodynamics, the VDROP model was adopted to simulate the DSDa, which closely matched the observed DSD. The present results reduce the empiricism of the DS1988 correlation.

Published papers for details:

Cui, F., Geng, X., Robinson, B., King, T., Lee, K., and Boufadel, M.C., 2020. Oil Droplet Dispersion under a Deep-Water Plunging Breaker: Experimental Measurement and Numerical Modeling. Journal of Marine Science and Engineering 8 (4), 230

Figure 1. Front views of the breaking wave at various times taken by the GoPro camera: (a) the steep Figure 3. Front views of the breaking wave at various times taken by the GoPro camera: (a) the steep wave crest; (b) the plunging breaker; (c) the secondary breaker, and (d) recovery of the free surface. wave crest; (b) the plunging breaker; (c) the secondary breaker, and (d) recovery of the free surface. 

Figure 2. The snapshots of oil dispersion experiments: (a) the original setup; (b) the plunging breaker hitting the oil plume (within the yellow circle); (c) the dispersed oil being transported to the measurement window of the shadowgraph camera; (d) underwater view of entrained oil droplets, and (e) side view of entrained oil droplets, and (e) side view of entrained oil droplets and remained oil films. 

Figure 3. An example of the photos of oil droplets and bubbles taken by the shadowgraph camera. The dark disks represent droplets of different diameters, and the hollow circles illustrate the air bubbles. 

Figure 4. Droplet size distribution (DSD) obtained from the experiments and the VDROP simulation. (a) Volume-based DSD and (b) number-based DSD (note the logarithmic scale in (b)). Error bars in (a) represent the standard deviation of each size class. The power–law correlations reported in the literature  are also shown.

FRONT-0.5X.mp4

Move 1. Wavetank experiment of oil dispersion under a plunging breaker.

UnderWater-2X.mp4

Move 2. OIl dispersion beneath the water surface.

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