Numerical Simulation of the Deflection of Jellyfish due to Air Bubble Curtains
Bibliographic record
Abstract
Abstract Increased jellyfish blooms have gained more attention recently due to their tendency to clog seawater intake systems of nuclear power plant cooling stations and threaten marine fish farms. The Bubble Tubing for Jellyfish Deflection Project (BTJD) was conducted by C-CORE to investigate the efficiency of Canadian Pond.ca’s Bubble Tubing® for jellyfish deflection. A three-dimensional Computational Fluid Dynamics (CFD) model was developed to quantify air bubble curtain performance for jellyfish deflection. The commercial CFD package STAR-CCM+ was used for this purpose. The multi-phase (water and air) fluid flow dynamics were simulated by solving Reynolds Averaged Navier Stokes (RANS) equation. The Discrete Element Modeling (DEM) approach was utilized to model the individual jellyfish behavior. The CFD model was initially used to optimize the flume tank test design, and then the model was validated using the test results from the flume tank. CFD simulations covered a range of test parameters such as current speed, number of curtains, curtain spacing, bubbler angles and air flow rates. Good agreement was observed between the CFD simulations and flume tank test results. The developed CFD model can be utilized for similar applications at field scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".