Application of Wake Shielding Effects With a Finite Element Net Model in Determining Hydrodynamic Loading on Aquaculture Net Pens
Bibliographic record
Abstract
A net wake shielding and self shielding model has been developed to accurately estimate the hydrodynamic loading on fin-fish aquaculture installations in the dynamics simulation software package ProteusDS. The software was developed to determine the loads and motions of various ocean engineering systems in response to wave, wind and current conditions. The effect of containment net hydrodynamic wake shielding is important to avoid overly conservative estimation of loads on fish farm installations. The reduction in fluid velocity through a net can be significant in many cases, leading to decreased loading and changes in motion on downstream nets and mooring components. The developed wake shielding model uses a wake volume approach to estimate a reduction in flow velocity and hydrodynamic loading on downstream components within the wake volume. Self shielding effects of adjacent twines within a net are also considered, as interactions between netting twines can reduce hydro-dynamic loading on nets at certain angles of incidence to the oncoming flow. This paper presents the developments both of the wake shielding and self shielding models and demonstrates the capability to accurately predict current forces acting on successive net pens by comparing simulation results with published results from tank tests, as well as a comparison of measured tensions on mooring lines at a full scale fish farm. A method for determining netting drag coefficients based on Reynolds number variations is also presented and compared to experimental drag tests on planar nets to confirm its validity.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".