Numerical simulations of a passive flapping-hydrofoil turbine with a freesurface
Bibliographic record
Abstract
The flapping-hydrofoil turbine concept is a very promising technology to extract renewable energy from fluid flows and generate green electricity. Some of this turbine's advantages are that its geometry is well suited for shallow river flows and that its working principle depends solely on the interactions between the fluid, the structure, and the electrical generator. Indeed, to avoid complex mechanical coupling systems that are prone to friction, the blade is simply held by springs and interacts directly with the generator. As such, the pitching and heaving motions of the hydrofoil can be very sensitive to operating conditions and flow perturbations. Our previous work has shown that the passive flapping-hydrofoil turbine is a viable concept in a controlled environment, but the turbine has not been studied in real-life conditions. In particular, the interaction between the free surface and the turbine needs to be studied to ensure a proper behavior of the turbine in shallow water. To solve the fluid-structure interaction equations, a finite volume flow solver is used in which the motion of the body is governed by a two-degree-of-freedom solid body model. Broyden's algorithm is used to ensure stable and efficient fluid-solid coupling. This quasi-Newton algorithm allows problems with very strong interactions to be tackled without stability restrictions. This method has proven to be reliable in the past and the proposed work builds upon this methodology to incorporate the free-surface modelling to the passive flapping-hydrofoil turbine model. This presentation thus details the validation and verification process of this numerical methodology. Preliminary results regarding the effect of the free surface proximity on the turbine's performance and stability will also be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".