Tip flow evolution in a turbofan rotor for broadband noise diagnostic
Bibliographic record
Abstract
A tip flow evolution in a realistic turbofan rotor is studied numerically for subsonic (approach) and transonic (cutback) operating conditions. Large Eddy Simulations are performed for Source Diagnostic Test benchmark rotor blade. Single-blade computations are performed for the two operating conditions. The mean-flow results and the wall-pressure statistics are analyzed in the tip flow region of the rotor. The mean trajectory of the tip leakage vortex is traced in both cases. The mean results reveal the presence of a strong tip blockage at approach. For cutback, the flow in the upper part of the blade produces a shock-boundary layer interaction with a lambda-shock. At approach conditions, the unsteady results show an early break-up of the tip leakage vortex and an impingement of the tip vortices with the leading-edge and the tip region of the blade. At cutback the tip leakage vortex is stronger and interacts with the leading edge of the adjacent blade. The blade wall-pressure spectra reflect the impact of the tip vortices on the blade leading-edge. The far-field noise is computed upstream with FfowcsWilliams and Hawkings’ analogy using porous surface formulations. Downstream, the projection on acoustic duct modes is made to remove hydrodynamics spurious sources. Both methods showed a good comparison with experiments for the middle frequency range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".