Bibliographic record
Abstract
During high wind events, certain building elements have the potential to generate significant tonal noise that can be heard miles away. These aeroacoustic issues are due to an interaction/coupling between an unstable fluid flow and a feedback mechanism, usually being a resonant acoustic mode. Aeroacoustic issues are often easy to avoid when susceptible geometry can be identified during the early design process, but mitigation can be technically difficult and very costly once issues do occur. Because aeroacoustic issues are unique and often annoying, they are often considered newsworthy and can be the subject of significant publicity. A series of case studies of existing structures, which have experienced aeroacoustic problems from their surrounding wind climate, are presented. Through each of these case studies, the steps used to identify suspect features and the recommended remedial measures are presented. The methodology for identification of the suspect feature may include investigating trends in weather data, conducting spectral analysis of audio recordings, performing calculations of the natural frequencies of building features, and on-site observations and measurements. Various mitigation strategies are detailed, depending on the mechanism found to be generating the aeroacoustic noise.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".