A Parametric Study of a Fully Passive Oscillating Foil Turbine on a Swinging Arm in a Tandem Configuration
Bibliographic record
Abstract
A fully passive oscillating foil turbine on a swinging arm in a tandem configuration consisting of two NACA 0015 foils at both ends of its arm and operating in an incompressible flow at a Reynolds number of 3.9×106 is investigated with numerical simulations. The turbine is free to oscillate passively in response to hydrodynamic forces and structural reactions from springs and dampers. The passive motion of the tandem turbine arises from a transfer of energy from the flow, and this motion is solved using a fluid-structure algorithm coupling the Newtonian dynamics of the system with two-dimensional, unsteady, and Reynolds-averaged Navier–Stokes equations. The performance metrics, i.e., the efficiency and power coefficient, of the proposed turbine concept are explored with a momentum gradient ascent algorithm, which uses the near-optimal configuration of an equivalent single-foil concept from a previous study as a starting point. These starting configurations consist of tandem foils operating either under coupled flutter or stall flutter instabilities. The use of gears to adjust the equilibrium position of the pitching motion is also considered, resulting in a total of four baseline configurations. The best configuration found with the gradient ascent algorithm presents an efficiency value near 75% and a power coefficient of 1.46, showing the great potential of fully passive oscillating foil turbines operating in a tandem configuration and providing valuable insight for further development of this technology through three-dimensional simulations and prototype testing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".