Optimization of the Efficiency of Stall Control Using Air Injection for Centrifugal Compressors
Bibliographic record
Abstract
Numerical investigation of the optimization of the stall control efficiency for a high speed centrifugal compressor using air injection is presented. External air was injected close to the diffuser entrance at the shroud surface of the vaneless region. Injection was applied with mass flow rates of 0.7%, 1%, and 1.5% of the design inlet mass flow rate with six different angles of 0 deg, 10 deg, 20 deg, 30 deg, 40 deg, and 180 deg measured from the positive tangential direction at the vaneless region. Detailed comparisons were made between the case without using air injection and the different air injection cases by comparing velocity, pressure, and force fluctuations with time. Results showed that as the injection mass flow rate increases, the number of diffuser passages with reversed flow decreases for all cases of injection except for the case of reverse tangent injection. Results indicated that using angle of injection of 30 deg minimized the stall area and provided the least force fluctuations with no reversed flow compared to other injection angles. Finally, it was found that injecting air with mass flow rate of 1.5% of the inlet mass flow rate at an angle of 30 deg resulted in shifting of stall onset to a mass flow rate corresponding to 3.8 kg/s instead of 4 kg/s for a compressor without using air injection control.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".