Computational Multiphase Flow Modelling of Oil Spill Through a Hydrophobic Mesh
Bibliographic record
Abstract
The current technologies for handling oil spill clean-up vary in expense and effectiveness and are largely ineffective. Oil spills occur due to accidents from well-heads and damaged facilities in the creeks of the Nigerian Delta and along the coastline of waters where hydrocarbons are prospected all over the world. They are unexpected and known to cause irreparable damage to aquatic environments and marine life. The development of a hydrophobic mesh is proposed to prevent oil from spreading into larger areas and from reaching sensitive coastlines. This will help engineers and clean-up crews in their quest to find an appropriate response to a given oil spill scenario as they race against the clock to prevent further damage and improve the oil recovery process. The overall goal of this project is to create a Numerical Simulation of meshes that repel water and attract oil using ANSYS, a Finite Element Analysis software. The mesh was modelled as a porous medium that acts like a filter that retains water on one side while allowing the passage of oil through it. In the course of this work, appropriate materials selection in fluid flow analysis was carried out. Also, the flow domain geometry was developed in such a way as to simulate a system containing an oil-water interface. Next, domain discretization (meshing) was carried out appropriately. After which appropriate boundary conditions and operating conditions were implanted in the model. Fluent was then set to initialize and run calculations. After calculations were run, results were gotten. These results were then interpreted pictorially. It was seen that the velocity streamlines for the oil phase passed through the hydrophobic mesh, while the velocity streamlines for the water phase were repelled from the hydrophobic mesh wall.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".