The effect of motion on wind tunnel drag measurement for athletes
Bibliographic record
Abstract
To optimize the reduction of the drag area coefficient of a speed skater, mannequins in static positions wearing suits were tested in the NRC 2m × 3m Wind Tunnel to find the lowest drag for a given range of speeds. The results obtained from this study can only be interpreted and applied to a speed skater if the aerodynamic drag coefficient is not affected significantly by the motion of the skater. It has been assumed that, for a representative cadence or frequency of oscillation of a speed skater lower than 1 Hz which is representative of the motion in a race, the aerodynamics could be considered quasi-stationary. The quasi-steady state is defined by the flow that developed around the body and the wake both governing the aerodynamic drag compared to the contribution of the low frequency of oscillation of the body. To verify this hypothesis and to determine to what extent the quasi-steady assumption is valid for a human body in a speed skating position, a study was carried out in a wind tunnel using a moving life-size mannequin. A mechanism was devised so that a mannequin in a sidepush position could be oscillated to mimic the sinusoidal path of a speed skater. The amplitude of motion was kept constant and the frequencies of oscillation and wind speeds were changed to cover the equivalent of 500 m to 10,000 m races. The experiments have revealed that the variations of the drag area coefficient with wind speed were similar for static positions and for cases where the mannequin was oscillating at frequencies lower than 0.67 Hz or for a period of rotation of 1.5 s. The drag reduction occurred at the same wind speeds for the static and dynamic cases. This paper presents details of the experiments and a summary of the main findings. © 2012 Published by Elsevier Ltd.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".