How Applicable Are Turbulence Assumptions Used in the Tidal Energy Industry?
Bibliographic record
Abstract
As tidal current and marine hydro-kinetic energy converters start to be deployed in pre-commercial arrays, it is critical that the design conditions are properly characterised. Turbulence is known to influence fatigue loads and power production, so developers use turbulence models to generate unsteady flows in order to simulate device performance. Most such models construct a synthetic flow field using a combination of measured parameters and theoretical assumptions. The majority in use today are based on atmospheric flow conditions and may have limited applicability in tidal environments. In the present work, we compare key turbulence model assumptions (which are recommended by the tidal turbine standards and are used in design software) to turbulence measurements from two tidal test sites in Scotland and Canada. Here, we show that the two sites have different levels of conformity to theoretical models, with significant variability within nearby locations at the same site. The agreement with spectral models is shown to be depth-dependent. The vertical component spectrum is better represented by the Kaimal model, while the streamwise spectrum is better represented by the von Kármán model. With the exception of one site, the shear profiles follow a power law, although with a different exponent to that commonly assumed. Both sites show significant deviations from the theoretical length scales and isotropy ratios. Such deviations are likely to misrepresent the loads experienced by a device. These results highlight the turbulence characteristics at real deployment sites, which are not well represented by current models, and, hence, which must be determined using field measurements.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".