Numerical Investigation of Steady Air Injection Flow to Control Rotating Stall in Centrifugal Compressors
Bibliographic record
Abstract
This paper concerns the role of air injection method in stabilization and stall control in centrifugal compressors. The main aim is to find the best arrangement of air injection parameters such as injection angle and injection mass flow rate in order to optimize the injection performance for stabilizing the compressor and increasing the surge margin. Numerical model was built to simulate high speed transonic centrifugal compressor working at an operating point close to surge. Air was injected at 12 locations at the vaneless region between the impeller and the diffuser at shroud surface with 5 different injection angles and 3 different injection mass flow rates. Results showed that the best injection method is when using an injection angle of 30° with injection mass flow rate of 1.5% of the design mass flow rate and the worst injection method is the injection at angle of 180° (reverse tangent injection). Results also indicated that by using air injection, the number of stalled diffuser passages is decreased compared to the case of no injection. The most significant result of this paper is that using an angle of injection around twice the value of the diffuser vane angle gives the best results and makes the ideal correction of the fluid kinetic energy and fluid angle at the diffuser inlet. It was found that injecting air at an angle less than the diffuser vane angle weakens the effect of injection and doesn’t increase kinetic energy of the fluid at diffuser inlet. It was also found that injecting air at an angle larger than the diffuser vane angle corrects the fluid direction but, at the same time, decreases the fluid kinetic energy at diffuser inlet.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".