Optimal Tandem Configuration for Oscillating-Foils Hydrokinetic Turbine
Bibliographic record
Abstract
A numerical investigation based on 2D URANS simulations is performed in order to seek an optimal spatial configuration for two oscillating foils within a hydrokinetic turbine. The objective of the study is to maximize the power extraction efficiency of the turbine. Tandem spatial configurations are considered because in such arrangement both hydrofoils are sharing the same flow window, which allows the turbine to reach higher efficiencies. The relative positioning of the downstream foil oscillating in the wake shed by the upstream hydrofoil is seen to be critical. Indeed, favorable interactions between the downstream foil and the wake vortices may lead to unexpectedly high power-extraction efficiencies (up to 64%), while unfavorable interactions may cause the downstream foil to contribute negatively to the total power extracted. A global phase shift parameter is introduced to characterize the tandem configuration. This parameter combines the inter-foil spacing and motion phase-shift into a single term. It is found useful to predict additional favorable configurations based on known results for cases with similar upstream-foil wake behavior. A comparison with experimental data is provided. Numerical predictions are seen to overpredict the power extraction performance in some cases. This is likely due to the broken 2D coherence of vortices in the 3D reality which affects the vortex-induced velocities and the subsequent foil-wake interactions.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".