Assessment of Flow Control Strategies for Improving Centrifugal Compressor Efficiency
Bibliographic record
Abstract
This paper describes a preliminary assessment of two flow control strategies for improving the adiabatic efficiency of centrifugal compressors for aero-engine applications. Given that the diffuser loss and pressure recovery play and important role in centrifugal stage efficiency, a centrifugal compressor with “fishtail” pipe diffusers is chosen for the study. This type of diffuser, which is among the most efficient diffusers, turns the flow directly from a high-swirl radial flow toward an axial flow, thus providing a smaller outer compressor diameter. As such, they are ideal for aero-propulsion applications. Past researches indicate that the diffuser performance is very much dependent on the impeller exit flow (diffuser inlet flow) uniformity. Two passive candidate flow control strategies that could improve impeller exit flow uniformity are proposed, namely slots casing treatment near the impeller radial bend and flow recirculation with injection in this area. They are aimed at attenuating the significant low-momentum region near the shroud that grows from the radial bend to the impeller exit. Iterations of the two proposed flow control strategies were evaluated through unsteady RANS CFD simulations on a low-speed centrifugal compressor stage with fishtail pipe diffusers. A comparison in terms of component and stage performance as well as an analysis of the flow field was carried out from the simulation results of the early iterations of the two flow control strategies. They show that both strategies have good potential for improving impeller exit flow uniformity and reducing losses in the fishtail pipe diffusers. However, the casing treatment strategy is more promising for improving stage efficiency due to lower penalty in impeller 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".