Active local control of propeller-aircraft run-up noise
Bibliographic record
Abstract
Engine run-ups are part of the regular maintenance schedule at Vancouver International Airport. The noise generated by the run-ups propagates into neighboring communities, disturbing the residents. Active noise control is a potentially cost-effective alternative to passive methods, such as enclosures. Propeller aircraft generate low-frequency tonal noise that is highly compatible with active control. This paper presents a preliminary investigation of the feasibility and effectiveness of controlling run-up noise from propeller aircraft using local active control. Computer simulations for different configurations of multi-channel active-noise-control systems, aimed at reducing run-up noise in adjacent residential areas using a local-control strategy, were performed. These were based on an optimal configuration of a single-channel control system studied previously. The variations of the attenuation and amplification zones with the number of control channels, and with source/control-system geometry, were studied. Here, the aircraft was modeled using one or two sources, with monopole or multipole radiation patterns. Both free-field and half-space conditions were considered: for the configurations studied, results were similar in the two cases. In both cases, large triangular quiet zones, with local attenuations of 10 dB or more, were obtained when nine or more control channels were used. Increases of noise were predicted outside of these areas, but these were minimized as more control channels were employed. By combining predicted attenuations with measured noise spectra, noise levels after implementation of an active control system were estimated.
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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 | 0.000 |
| 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".