Two-way interaction between river and deployed cross-flow hydrokinetic turbines
Bibliographic record
Abstract
This study focuses on the interaction between the water free surface, the riverbed, and some Darrieus-type hydrokinetic turbines deployed in river flows. As turbines offer a resistance to the flow, they affect the upcoming velocity, which in turn affects their performance. The proximity of the neighboring deformable free surface or rigid bed may also influence their power extraction. In this context, 2D and 3D URANS simulations of a cross-flow (H-Darrieus type) turbine are conducted with free-surface modeling and adapted boundary conditions allowing the capture of the interactions between the turbine and the resource. Different water depth immersions are considered in order to study local proximity effects. It is found, neglecting riverbed friction, that shallow immersion is detrimental to power extraction whereas bed proximity associated with deep immersion is favorable. This observation does not hold when considering a more realistic river with a velocity profile throughout the depth. Direction of rotation in high proximity cases also plays a role. Although the literature suggests a slight increase in power extraction with the Froude number, we find that when interaction with the resource is taken into account, the power extraction is rather independent of the Froude number for deep immersion or slightly decreasing for shallow immersion. Nonetheless, all the variations in power extraction reported in this study remain small compared to the ones associated with blockage effects. Finally, the shallow immersion case simulated in 3D behaves similarly to that simulated in 2D. Switching the orientation of the rotation axis from horizontal to vertical, despite changing the local interaction with the free surface, does not affect significantly the performance of the turbine.
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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) | 0.001 | 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 | 0.001 |
| 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".