The dynamic flow behaviour of an oar blade in motion using a hydrodynamics-based shell-velocity-coupled model of a rowing stroke
Bibliographic record
Abstract
The flow around a rowing oar blade during a stroke is highly complex owing to the proximity of the water surface and the rapidly changing blade flow incidence (here, greater than 180° in under 0.75s). This flow is simulated using a computational fluid dynamics (CFD) model with a rotating subdomain for blade rotation coupled to a model of the shell velocity. Based on the shell velocity and a specified oar angular velocity, the CFD model calculates the highly unsteady three-dimensional flow, providing instantaneous drag, lift, and propulsive forces on the blade. The propulsive force drives the shell velocity model, which also accounts for the shell drag and the motion of the rowers relative to the shell. The dynamic blade—water interaction is depicted in six distinct flow regimes, characterized by the relative motion of the blade and the temporal influence of drag and lift. It is seen that the propulsive force generated by the blade is largely lift induced during the first half of the stroke. Dynamic stall behaviour of the blade characterizes the flow during the second half of the stroke, where drag increasingly influences the propulsive force. At the end of the stroke, the propulsive force is once again largely lift induced.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".