Numerical Simulation of Aerodynamic Instabilities in a Multistage High-Speed High-Pressure Compressor on Its Test-Rig—Part I: Rotating Stall
Bibliographic record
Abstract
Aerodynamic instabilities such as stall and surge may lead to mechanical failures. They can be avoided by better understanding and accurate prediction of the associated flow phenomena. Numerical simulations of rotating stall do not often match well the experiments as the number of cells and/or their rotational speed are not correctly predicted. The volumes surrounding the compressor have known effects on rotating stall flow patterns; therefore, an increased need for more realistic simulations has emerged. In that context, this paper addresses a comparison of numerical stall simulation in a compressor alone with a numerical stall simulation including the additional compressor rig. This study investigates the influence of the upstream and downstream volumes of the compressor rig on the rotating stall flow patterns and the consequences on surge inception in a high-pressure, high-speed research compressor. The numerical simulations were conducted using an implicit, time-accurate, 3D compressible Reynolds-averaged Navier–Stokes (URANS) solver. First, rotating stall is simulated in both configurations, and then the outlet nozzles are further closed to bring the compressors to surge. The numerical results show that when the compressor rig is accounted for, fewer cells develop in the third stage and their rotational speed is slightly higher. The major difference linked to the presence of the rig lays in the existence of a 1D low frequency oscillation of the static pressure, which affects the entire flow and modifies surge inception. The analysis of the results leads to a calculation of the thermo-acoustic modes in the whole configuration, which shows that this low frequency corresponds to the third thermo-acoustic mode of the complete test-rig.
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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 | 0.001 | 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) | 0.002 | 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".