Boundary-Layer Transition Affected by Surface Roughness and Free-Stream Turbulence
Bibliographic record
Abstract
This paper presents experimental results documenting the effects of surface roughness and free-stream turbulence on boundary-layer transition. The experiments were conducted on a flat surface, upon which a pressure distribution similar to those prevailing on the suction side of low-pressure turbine blades was imposed. The test matrix consists of five variations in the roughness conditions, at each of three free-stream turbulence intensities (approximately 0.5%, 2.5%, and 4.5%), and two flow Reynolds numbers of 350,000 and 470,000. The ranges of these parameters considered in the study, which are typical of low-pressure turbines, resulted in both attached-flow and separation-bubble transition. The focus of the paper is on separation-bubble transition, but the few attached-flow test cases that occurred under high roughness and free-stream turbulence conditions are also presented for completeness of the test matrix. Based on the experimental results, the effects of surface roughness on the location of transition onset and the rate of transition are quantified, and the sensitivity of these effects to free-stream turbulence is established. The Tollmien–Schlichting instability mechanism is shown to be responsible for transition in each of the test cases presented. The root-mean-square height of the surface roughness elements, their planform size and spacing, and the skewness (bias towards depression or protrusion roughness) of the roughness distribution are shown to be relevant to quantifying the effects of roughness on the transition process.
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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 | 0.001 |
| 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".