Simulation of a High-Energy Finfish Aquaculture Site Using a Finite Element Net Model
Bibliographic record
Abstract
Over half of the seafood in the world today is produced through aquaculture. Many finfish species, such as Atlantic salmon, are farmed in permeable net pens in the ocean. Traditionally, these sites have been located in regions protected from high energy ocean swell, current, and wind. However, in areas such as Nova Scotia, Canada, there is a declining number of such protected locations available and so aquaculturists are moving into exposed sites. To safely operate at these sites, it is necessary to engineer the pens to withstand the forces of the open ocean. To conclusively assess finfish aquaculture equipment and moorings in open ocean conditions, Dynamic Systems Analysis Ltd. (DSA) has developed a finite-element net model (FENM) to interface with the dynamics simulation software ProteusDS. The following paper presents the development of the FENM and demonstrates the capability of the FENM to model wave and current loadings by comparing FENM simulations with published results from tank tests. In addition, the results of simulations of a full scale finfish aquaculture site in hurricane conditions are presented. The conditions were collected with an acoustic Doppler current profiler during hurricane Earl on September 4, 2010. The mooring line tensions from the ProteusDS simulation are compared against tensions measured during the storm with a Submersible Tension Logger (STL).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".