Wind and tall buildings: negatives and positives
Bibliographic record
Abstract
Abstract Wind is often regarded as the foe of tall buildings since it tends to be the governing lateral load. Careful aerodynamic design of tall buildings through wind tunnel testing can greatly reduce wind loads and their affect on building motions. Various shaping strategies are discussed, aimed particularly at suppression of vortex shedding since it is frequently the cause of crosswind excitation. The use of supplementary damping systems is another approach that takes the energy out of building motions and reduces loads. Different applications of damping systems are described on several buildings, and an example of material savings and reduced carbon emissions is given. Wind also has some potential beneficial effects particularly to tall buildings. One is that, since wind speeds are higher at the heights of tall buildings, the potential for extracting wind energy using wind turbines is significantly improved compared with ground level. This paper explores how much energy might be generated in this way relative to the building's energy usage. Other benefits are to be found in judicious use of natural ventilation, sometimes involving double‐layer wall systems, and, in hot climates, the combination of tailored wind and shade conditions to improve outdoor comfort near tall buildings and on balconies and terraces. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".