A partial-turbulence approach to estimate peak wind loads on low-rise building roofs
Bibliographic record
Abstract
Quasi-steady (QS) theory is a commonly-used tool in wind engineering for the assessment of wind loads on structures induced by large-scale gusts. However, the QS-based approach tends to underestimate peak loads significantly since it fails to capture the effects of small-scale and body-generated turbulence. The objective of this thesis is to develop a method to estimate peak wind loads on low-rise roofs based on a partial-turbulence approach, i.e., using the QS vector model for loads induced by large-scale fluctuations from the incident flow, and a separate statistical model to account for the effects due to body-generated turbulence.\nWind tunnel tests have been conducted for four 1:50 scaled low-rise building models with gable and hip roofs. Roof slopes range from 5:12 to 12:12. Data of similar tests on a flat roof model conducted by Wu and Kopp (2016) is taken for comparison. It is found that the smallest scale that QS vector models can reach is about 5H (H denotes the mean roof height) in length, while the largest scale affected by the body-generated turbulence can be up to 30H. The performance of QS vector models is found to be better on flat roofs than sloped roofs, and is closely related to the type of aerodynamics at different locations. Specifically, it is found to work reasonably well in regions of flow separation, but less well for flow reattachment and positive pressure zones on the windward faces of the 12:12 sloped roofs.\nA statistical model has been developed to account for the pressure component induced by the body-generated turbulence, which is found to be dependent on both nominal wind directions and terrain conditions, and the differences can be minimized by normalizing it with the turbulence kinetic energy and the Quasi-Steady pressure coefficients. The final model takes the form of a 3-parameter T-scale distribution. It is valid for panels that are governed by suction loads due to flow separation and can work across roof shapes and terrains. By combining this model with the QS vector model, a method is developed to estimate peak pressure coefficients using a Monte-Carlo approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".