Numerical Predictions of Propeller-Wing Interaction Induced Noise in Cruise and Off-Design Conditions
Bibliographic record
Abstract
Using numerical methods, the aeroacoustic field induced by the interaction of a 4-bladed NASA SR-2 propeller and its wake with a wing is investigated under cruise conditions (Mach 0.6).The SmartRotor code, a coupled vortex particle and panel method, which is integrated with an acoustic solver based on the Farassat 1A formulation of the Ffowcs-Williams Hawkings equation, was used.Three main areas were investigated: the effect of propeller tip geometry on the propeller's wake and blade tip vortex; the effect of integrating a wing in the tractor configuration, including the effect of its position, and wing local leading edge sweep; and the effect of operating the combined wing/propeller system in off-design conditions such as low forward speed or in non-axial inflow.It was discovered that tip sweep is effective at reducing propeller tip vortex strength with no adverse effect on noise.Modifying tip dihedral was found to always increase tip vortex strength.Integrating a wing in the wake of the propeller increased the broadband noise generated, but had little effect on harmonic noise.The downstream position of the wing was found to not affect noise while vertical offset from the propeller axis increased noise.The most important discovery was that applying local leading edge sweep to the wing in the region of the propeller's wake decreases noise proportionally to the change in the angle between the helical tip vortex and the wing's leading edge.These noise reductions were on the order of up to 1.3 dB for overall sound pressure level, and 7.5 dB at the blade passage frequency.
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 | 0.002 |
| 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 | 0.001 |
| 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".