Flow Visualization and PIV Measurements of Flow Around a Sport Utility Vehicle Model
Bibliographic record
Abstract
The instantaneous flow structure around a 1:18 scaled model of a square-back sport utility vehicle (Hummer H2) was documented in a low-speed water tunnel. The study comprises both flows with the model fixed on a flat plate and flows with the model’s wheels rolling on an endless belt that moved with the speed of the free stream, thus simulating ground effects. The flow structure was investigated using flow visualization by dye injection as well as particle image velocimetry (PIV) for several Reynolds numbers in the range of 7000 to 27700. The flow along the roof, the sidewalls, and the underbody was observed to separate at the rear edges of the body, creating a recirculation zone at the rear of the vehicle, which is associated with pressure loss and a major contribution to aerodynamic drag. In the vertical plane of symmetry, this recirculation zone appears as two counter-rotating vortices. With a fixed ground, the lower vortex was less energetic than the upper vortex because the boundary layer that developed along the ground upstream of the model reduced the momentum of the flow below the vehicle. This boundary layer was also observed to separate from the ground behind the vehicle, creating a third vortex located further downstream along the ground. This boundary layer separation forced the bottom vortex to remain attached to the base of the vehicle, whereas the upper vortex was advected in the wake. The dimensionless frequency (Strouhal number) of the vortex shedding process from the roof was found to be in the range of 0.1 to 0.9. With a moving ground, the upper vortex behaved similarly to that in the fixed ground configuration; however, in the absence of the boundary layer along the ground, the lower vortex was typically stronger and its location showed some variability. In both configurations, the Reynolds number had little influence on the wake topology, mostly increasing the turbulence intensity without modifying the main flow pattern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".