Large Eddy Simulation of an Energetic Tidal Strait with Device-Scale Turbulence
Bibliographic record
Abstract
Tidal channels suitable for tidal power devel- opments exhibit complex, turbulent flow, at high Reynolds numbers with dynamic features over a wide range of scales that can persist for several hours. Modelling such flow plays a key part in characterising the conditions that tidal turbines will experience in situ. However, simulations of these channels are extremely challenging, and many numerical models compromise on either fidelity or size to keep computational complexity down to a manageable scale. New numerical techniques are required to overcome these restrictions, and provide the tidal energy industry with valuable insights into the marine environment. This paper presents a non-hydrostatic, high-fidelity com- putational fluid dynamics model of the Grand Passage in the Bay of Fundy, Canada, using the coastal and tur- bine modelling software CoastED. The model employs a Discontinuous Galerkin finite element formulation of the Navier-Stokes momentum equation, coupled with a Vreman subgrid eddy viscosity model, a variant of Large Eddy Simulation adapted for anisotropic grids. This, along with the use of a novel unstructured grid scheme, allows flow features from centimetres to kilometres to be captured over several M2 tidal cycles. By comparing virtual Acoustic Doppler Current Profiler (ADCP) results data with measurements from real ADCPs in the Grand Passage, we show that the model is effective in recreating aspects of the tidal currents often missed in hydrostatic simulations. We also examine some of larger modelled tidal flow features, and contrast them with evi- dence from satellite data.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".