Aeroacoustic investigations of a rotor-beam configuration in small-size drones
Bibliographic record
Abstract
Various aeroacoustic mechanisms involved in a rotor-beam configuration typically encountered in small-size drones in hover conditions are investigated both numerically and analytically, complemented with experimental data. High-fidelity lattice-Boltzmann method (LBM) simulations are performed on the complete experimental setup, capturing both the aerodynamic and the acoustic features of the configuration. The far-field noise is obtained by applying the Ffowcs Williams and Hawkings (FW-H) acoustic analogy. The rotor noise is also modeled as the sum of thickness noise, steady and unsteady loading noise corresponding to potential interactions between the blades and the beam. The analytical model of rotor noise relies on a strip theory, combining input velocity profiles from LBM and Sears's blade response function for each strip, and the FW-H analogy formulated in the frequency domain. The beam noise is modeled using a similar strip theory and a response model to the circulation of passing blades, based on the incompressible potential flow theory around a circular cylinder. Aerodynamic and acoustic results from the simulation and the models are in good agreement with measurements. Unsteady loading noise is found dominant for all tones for the present rotor-beam configuration corresponding to a small chord-to-beam diameter ratio. The three-dimensional directivities of some sound harmonics also have a unique wavy pattern in the rotor plane.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".