Aeroacoustic Numerical Investigation of a Scaled Compressor Cascade
Bibliographic record
Abstract
In order to further develop technologies to reduce noise emissions of aero engines, an understanding of the noise propagation through compressor blade rows of modern turbofan engines is of major importance. To enable more detailed experimental investigations of the sound propagation in aero engines, engine components or stages have to be scaled for an installation into test rigs that allow for experiments under acoustically optimized boundary conditions. The main focus of the present work is thus to discuss a scaling approach that ensures both aerodynamic and aeroacoustic similarity between a given test rig and engine. For that purpose, a stator row of a four-stage high-speed axial compressor (4AC) based on the test rig at the Institute of Turbomachinery and Fluid Dynamics (TFD) at the Leibniz University Hanover is scaled to fit into the TFD's Aeroacoustic Wind Tunnel (AWT). Numerical investigations based on multiple modeling approaches are performed to verify a similar aeroacoustic behaviour in both test rigs. Reynolds-Averaged Navier-Stokes (RANS) and Unsteady-RANS simulations are carried out to assess the aerodynamic characteristics of the blade rows. The aeroacoustic modelling consists of simulations with an Euler acoustic solver to compare the modal transmis-1 The presented work was divided equally between the Institute of Turbomachinery and Fluid Dynamics in Hanover and the Mechanical Engineering Department in Sherbrooke (stefanie.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".