Performance and near-wake characteristics of a vertical-axis hydrokinetic turbine under a turbulent inflow and free-surface
Bibliographic record
Abstract
In this work we investigate the performance and near-wake characteristics of a full-scale vertical-axis tidal turbine under a uniform inflow and turbulent inflow.The governing equations are the incompressible Navier-Stokes equations expressed within an arbitrary Lagrangian-Eulerian (ALE) framework.The finite-element based variational multiscale (VMS) formulation, augmented with a weakly imposed Dirichlet boundary condition at no-slip surfaces, is used.A turbulent inflow is prescribed using a synthetic turbulence generation (STG) method referred to as Smirnov's random flow generation and the near-wake characteristics are studied using a multi-domain method.While the performance of the turbine slightly reduced under a turbulent inflow compared to a uniform inflow, there was a negligible difference in its performance between the two turbulent inflow conditions.A turbulent inflow also resulted in large fluctuations of the instantaneous power coefficient which has important implications for the fatigue life of certain components.Lastly, the wake recovery was notably improved under a turbulent inflow suggesting that a shorter streamwise inter-device spacing may be acceptable in highly turbulent tidal sites.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".