PIV Analysis of Wake Characteristics of Slanted-Back Ahmed Bodies: Effect of Leading-Edge Shape
Bibliographic record
Abstract
Abstract This study experimentally investigates turbulent flow separation over a slanted-back Ahmed body with different leading-edge configurations (rounded and square) using the particle image velocimetry (PIV) technique. Reynolds number (based on free-stream velocity and body height) is \({\text{Re}}_{\text{H}}\) = 0.17 × 105. Spatiotemporal flow characteristics, including mean flow, vorticity flux, spatial two-point correlation, reverse flow area, turbulent kinetic energy budget, frequency spectra, and proper orthogonal decomposition (POD) are analyzed. The results reveal a larger recirculation region near the leading edge of the square leading-edge (SL) case, associated with higher vorticity flux compared to the rounded leading-edge (RL) case. Pulsations are observed in the wake region recirculation bubbles through phase-averaging analysis of instantaneous velocity and vorticity. The auto and cross-correlation of reverse flow areas in the SL case exhibit higher temporal correlations in the leading edge and wake region. The analysis of Kelvin-Helmholtz wavelength and frequency spectra indicates a smaller wavelength in the RL case, corresponding to a higher dominant shedding frequency than the SL case. POD reveals the formation of smaller coherent structures with smaller convective velocities, and higher shedding frequency in the wake region of the RL case.
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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) | 0.000 | 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".