Aeroacoustic Noise from Building Façades – Observed Problems and Approaches to Mitigation
Bibliographic record
Abstract
As wind passes over and around a building there are aerodynamic interactions between the airflow and building features. This includes local accelerations of flows, the generation of turbulence, and the potential formation of vortices in the wake of building features. Under certain conditions this interaction can result in the generation of audible tones. If the amplitude of the generated tone is sufficiently high, and its characteristics are distinctly different from the existing background noise level, disturbance to the building and its surrounding environment is possible. Several phenomena are attributed to the generation of aerodynamic noise. Many of these relate to physical conditions that are often exploited in musical acoustics to produce an efficient generation of sound by coupling airflow or vibration to a resonant object. Whereas in musical acoustics the instruments are tuned to produce specific pitch and timbre, conditions of aerodynamic noise generated by buildings are generally unexpected, unwanted, and unmusical. When these problems occur, they often become publicized due to noise complaints, and can lead to loss of revenues, costly remediation, and potential damage to the reputation of the building owner and designers. As such, there is a desire to identify risk during the design of the building and take appropriate steps to mitigation of risk where noise generation is possible. In this presentation we discuss the common sources of aeroacoustic resonances that occur on building façade features, some of the techniques used to establish risk of noise problems, and approaches to managing risk and mitigation measures. Examples are presented from projects around the globe that include some of the world’s tallest buildings. Numerical and experimental methods of assessment are reviewed and a general framework for assessment is presented.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".