Comparison between URANS simulations and an analytical model for predicting the blade pressure distribution
Bibliographic record
Abstract
In order to improve the prediction of fan tonal and broadband noise, the use of unsteady numerical simulations to predict the unsteady pressure loading on a blade is evaluated and compared to a recently developed analytical model for a flat plate cascade at zero angle of attack submitted to 3D gusts. Unsteady Reynolds-Averaged Navier-Stokes data on actual blades should account for more realistic blades parameters such as blade thickness and camber and consequently actual loading. The accuracy of the turbomachinery flow solver Turb’Flow is first checked on the mean loading of a typical thin compressor blade. Unsteady simulations are then made on two simplified flat-plate stator geometries where pseudo rotor wakes are impinging on the vanes. The comparison of the unsteady numerical pressure jumps on the stator vane with the prediction of the analytical model yields encouraging results, and the effect of the vane thickness is evaluated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".