The Role of Streamwise Vorticity in Flows Over Turbomachine Blade Suction Surfaces
Bibliographic record
Abstract
Streamwise streaks and vortices are frequently encountered in low Reynolds number flows over blading. Observations have shown that, in addition to flows over concave pressure surfaces, convex suction surfaces are also influenced by streamwise vortices. These observations are based on surface flow visualization studies and computational work with highly resolved Large Eddy Simulation. Fine scale organized streaks exist in the laminar regions of turbine and compressor blading and are predictable. For a turbine blade with a blunt leading edge, at Reynolds numbers typical of aircraft cruise conditions, the streamwise vorticity may persist, on a time-average basis, to influence the entire suction surface. Time resolution is required to capture the flow complexity that is fundamental for an understanding of the physical behavior of the laminar boundary layer and its separation and transition. Progress has been made in modeling and predicting transition and laminar separation and the new findings of interesting vortical behavior need to be incorporated. In the leading edge region spanwise vorticity may promote early transition and bubble closure; further downstream streamwise vorticity may become established. The physics of this streamwise vorticity imposes severe requirements on the temporal and spatial resolution of both experimental and computational methods. A narrow spanwise computational strip does not allow the streamwise vorticity to settle into an organized pattern; if it is to become organized, an adequate spanwise domain is required.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".