Casing Treatment for Desensitization of Compressor Performance and Stability to Tip Clearance
Bibliographic record
Abstract
This paper presents the development of a novel casing treatment to reduce compressor performance and stall margin sensitivities to tip clearance increase. A linked research project on blade design strategies for desensitization had discovered two flow features that reduce sensitivity to tip clearance, namely increased incoming meridional momentum in the rotor tip region and reduction/elimination of double tip leakage flow. Double tip leakage flow is the flow that exits one tip clearance and enters the tip clearance of the circumferentially adjacent blade instead of convecting downstream out of the blade passage. A new and practical casing treatment was developed and analyzed through Reynolds-averaged Navier–Stokes (RANS) computational fluid dynamics (CFD) simulations to decrease double tip leakage and reduce or even eliminate performance and stall margin sensitivity to tip clearance size. The casing treatment design consists of sawtooth-shaped circumferential indentations placed on the shroud over the rotor with a depth on the order of the tip clearance size. A detailed analysis of the flow field allowed for the elucidation of the flow mechanism associated with this casing treatment. A computational parametric study gave preliminary design rules for minimizing both performance/stall margin sensitivity to tip clearance and nominal performance loss. An improved casing indentation design was produced for which CFD simulations showed a complete desensitization of pressure ratio and stall margin while reducing efficiency sensitivity significantly for the tip clearance range studied with only a very small penalty in nominal pressure ratio. Further simulations showed that this casing treatment can be combined with desensitizing blade design strategies to further reduce tip sensitivity and reduce/eliminate/reverse nominal performance penalty. Lastly, preliminary CFD simulations on an axial compressor stage indicate that this shallow indentations' casing treatment strategy remains effective in a stage environment.
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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 | 0.001 |
| 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".