Attenuation of shear-layer instabilities in steady and pulsatile axisymmetric shear-thinning flows
Bibliographic record
Abstract
The current study characterizes the attenuation of instabilities in steady and unsteady shear layers by investigating shear-thinning flows downstream of a confined axisymmetric sudden expansion. Flow fields were captured using particle image velocimetry. Tested fluids exhibited approximate power-law indices of 1, 0.81, 0.61 and 0.47 and measurements were performed at mean throat-based Reynolds numbers of ${Re_m} = 4800$ and 14 400. Unsteady flows were tested at a Strouhal number and amplitude-to-mean velocity ratio of $St = 0.15$ and $\lambda = 0.95$ , respectively. For unsteady shear layers, shear-layer roll-up regardless of shear-thinning strength was evidenced by collapse of average circulation over time. For steady shear layers, consistent shear-layer behaviour regardless of shear-thinning strength was evidenced by similar shear-layer trajectories and by growth rates in vorticity thickness. However, vorticity fields of the unsteady and steady shear layers, standard deviations of shear-layer trajectory, thickness of steady shear layers and Reynolds shear-stress spectra of the steady shear layers reveal an attenuation of shear-layer instabilities not captured by Reynolds number. Specifically, shear-layer instabilities exhibit increased diffusion with increasing shear-thinning strength and, in the case of steady shear layers, shear-thinning strength is shown to promote shear-layer stabilization. Also, evidenced by vorticity fields and through Reynolds shear-stress spectra, instabilities frequently coalesce into large rollers, a result that would suggest the presence of an inverse eddy cascade. The behaviour of shear-thinning fluids is shown to stabilize shear layers through attenuating shear-layer instabilities, complementing observations from previous studies showing how shear-thinning fluids promote turbulence in the dominant flow direction.
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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 | 0.000 |
| 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 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".