Examining the Symmetry of a Turbulent Wake Arising From Asymmetric Turbulent Shear Layers: An Experimental and Analytical Study
Bibliographic record
Abstract
Abstract This study examines the wake of a plate subject to asymmetric boundary layers at the trailing edge. In contrast to previous studies, this work varies shear layer asymmetry using tripping wires while keeping the Reynolds number and pressure constant. Six distinct conditions are considered, encompassing the natural wake and asymmetric wakes for five different values of θ/θo where θ is the boundary layer momentum thickness of the disturbed upper plate side, while θo is momentum thickness of the natural boundary on the lower plate side. Both boundary layers are turbulent, and the wake flow statistics were measured at downstream position, x/h. These conditions were studied to gain deeper insight into wake evolution and self-similarity. Significant changes in velocity profiles were observed with increasing momentum thickness ratio. The asymmetric wake showed distinct differences, including a velocity defect (Ud = Ue – Ū) where Ue is edge velocity of the wake and Ū is mean velocity and higher shear stress compared to the symmetric case. The study suggests the presence of long memory in wakes due to self-preserving states established by initial conditions. Developing analytic methods in conjunction with Reynolds stress profile and Reynolds stress shape functions, we were able to formulate a self-similarity equation for the wake.
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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 | 0.001 |
| 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 | 0.001 |
| 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".