Numerical Simulation of Using Combined Active and Passive Stall Control Techniques in Centrifugal Compressors
Bibliographic record
Abstract
This study presents a numerical simulation of stall control by using the casing treatment method which represents the passive control technique and also by using the air injection method which represents the active control technique. The casing treatment types used are the holed casing treatment and the casing skewed slots. There are four proposed numerical models; the first model is the regular shape model without casing treatments, the second model combines the holed casing treatment with the skewed slots, the third model is similar to the second one but enhanced by air injection at specified locations at the slot wall while the fourth model contains casing slots only. Results showed that the holed casing treatment helps in reducing the tip leakage flow by transferring it from the blades tip area to the impeller inlet. Also, it was found that the casing slots can cause a flow circulation and increase the kinetic energy around the position of slots and this leads to decreasing the number of stall cells. Results indicated also that the air injection at the casing slots increases the circulated flow rate inside the slots and minimizes the flow separation. It was found that the third model which contains casing slots only is the best comparing with other models in terms of decreasing the stall areas and increasing the compressor stability. Final results confirmed also that the casing treatment method increases the stability but reduces the isentropic efficiency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".