Bibliographic record
Abstract
The present work explores the effects of surface imperfections on the transition to turbulence of an incompressible boundary layer over a flat-plate. The analysis focuses on flow mean-dynamics. Visualization of instantaneous coherent structures provides insight into the flow evolution. Geometries considered include forward-facing steps and a step-cavity, representative of roughness commonly seen in manufacturing; the step-sizes and cavity depth are a small fraction of the local boundary layer thickness. A series of well-resolved direct numerical simulations are performed. A controlled Klebanoff-type transition is initiated via a narrow vibrating ribbon placed upstream of the surface imperfection. To distinguish the impact of the forward-facing step and the effect of the cavity, data from a flat-plate and medium-height backward-facing step cases from a previous study [M. Teng and U. Piomelli, “Instability and transition of a boundary layer over a backward-facing step,” Fluids 7, 35 (2022).] is employed as a reference for comparison. The perturbations are found to be locally stabilized, and transition inception is delayed for the medium-height forward-facing step, whereas in all other cases, increased growth-rates promote the onset of transition. The evolution of flow structures in the step-cavity case resembles that of the medium-height backward-facing step: the Kelvin–Helmholtz instability is a predominant mechanism that drives the amplification in the separation region. The phenomenon of stabilization and destabilization is explained from the perspective of energy budget analysis. Although the active instability mechanisms for each surface imperfection are locally influential, the route to turbulence via the Klebanoff regime remains qualitatively the same, independent of stabilizing or destabilizing effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".