Optimal Operating Parameters for an Oscillating Foil Turbine at Reynolds Number 500,000
Bibliographic record
Abstract
The use of oscillating foils to develop turbines to extract energy from an oncoming flow has recently been found competitive with rotor blade designs. Moreover, the rectangular extraction plane of the oscillating-foil turbine presents clear advantages in the field of hydrokinetic turbines. The present study, based on two-dimensional unsteady Reynolds-averaged Navier–Stokes numerical simulations, focuses on the range of operating parameters maximizing power extracted by a single oscillating foil for small-scale application Reynolds number. The power-extraction efficiency is mapped in parametric spaces associated with different motion amplitudes. Efficiency maxima reach about 43% in all the parametric spaces considered, but for different values of pitching amplitude and frequency. It is found that the best parametric zones in all cases exhibit similar characteristics with regards to the effective angle of attack. Indeed, they are all associated with a maximum effective angle of attack of about 33 deg and a maximum (normalized) rate of change of the latter of 0.55. It is further observed that, contrary to laminar results previously published, the occurrence of leading-edge vortex shedding is not necessarily observed in optimal cases at these higher Reynolds number conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".