Compressor Tip Leakage Mechanisms
Bibliographic record
Abstract
Abstract The over tip leakage flow in an unshrouded compressor blade row is highly three dimensional, yet in order for aerodynamicists to analyze and improve designs, they must be able to simplify the problem down to a limited number of mechanisms. In this article, the behaviors of the dominant loss mechanisms are investigated using a multi-order methodology that combines rapid experimental tests of different geometries, detailed measurements in a large rotating rig, large numbers of industry-standard 3D Reynolds-averaged Navier–Stokes (RANS) simulations, and a single direct numerical simulation (DNS) of the datum geometry. The three loss mechanisms identified are ultimately caused by mismatches in flow velocity: separation of the flow as it enters the gap, mixing of the leakage jet with the mainstream close to the suction surface, and endwall shear acting on the jet itself at mid passage. This article is presented in three sections: First, the loss mechanisms are visualized and examined in detail using experiment, simulation, and models. Second, the uncertainty in industry-standard predictions is analyzed and improvements to turbulence modeling are presented. Finally, a matrix of blades with different 3D designs is used to investigate the balance of loss mechanisms and a reduction in total loss generation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".