Experimental studies of the effects of hydrophobic coatings on flow separation around 3D bluff bodies
Bibliographic record
Abstract
Abstract This study explores how hydrophobic coating influences the structure of the flow separation at the rear end of 3D bluff bodies. Two 3D representative bluff bodies, namely the standard Ahmed body (SAB), which features flow separation and reattachment at its slant surface, and the elliptical Ahmed body (EAB), which exhibits fully separated flow, are employed. The bluff bodies were coated with Ultra Ever Dry hydrophobic paint. Experiments were carried out on pairs of coated and uncoated SABs and EABs in a water tunnel utilizing time-resolved and standard particle image velocimetry (PIV) at a Reynolds number of 4.3 × 10 4 based on the model height. The results show that hydrophobic coatings influence the flow features of these bluff bodies. For the SAB, the coating alters the slant separation bubble, increasing the reattachment length by 80% and reducing the shear stress. The Strouhal number on the slant surface of the SAB also increases, with a dominant value of S t = 0.24. Proper orthogonal decomposition (POD) analysis shows dominant Strouhal numbers of S t = 0.36 and S t = 0.48 for the first and second POD modes, respectively. Additionally, dynamic mode decomposition (DMD) analysis identifies a dominant Strouhal number of S t = 0.3 in the wake. Conversely, the EAB, which already has a fully separated flow, is less affected by the coating. The wake recirculation length is reduced by 6%. Strouhal numbers on the coated EAB’s slant surface range from 0.40 to 0.55, and those in the wake vary from 0.25 to 0.85. The POD analysis does not reveal dominant Strouhal numbers in the EAB’s wake, while the DMD analysis indicates a dominant Strouhal number of S t = 0.013, pointing to energetic modes due to the fully separated flow. These findings demonstrate that hydrophobic coatings affect the flow characteristics of 3D bluff bodies differently, depending on their inherent flow separation properties.
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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".