Aerodynamic mitigation of low-rise building with complex roof geometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During strong wind events, building roofs are subjected to high wind uplift forces (suctions), which often lead to severe roofing component damage, or even roof total failure, flying debris, and water intrusion, hence, interior damages. Typical roof shapes (e.g., gable and hip) are generally designed using provision codes and standards to accurately estimate peak load impacting the roofs during wind events for design purposes. Complex roof geometry can be efficiently examined using wind tunnel testing and computational modeling to provide quantitative assessment for wind to narrow down the design alternatives and to examine the improvement gained from mitigation techniques. In this study, an isolated low-rise building with a complex roof shape is examined using large eddy simulation (LES) to numerically assess wind load prediction by validating it with wind tunnel results. This study presents two roof modification scenarios using parapets added to roof corners and ridgelines to displace the flow from the separation locations to reduce the wind impact on the roof. The current study aims to 1) evaluate wind load on an isolated low-rise building with complex roof geometry for various angles of attack and 2) mitigate the roof aerodynamically using parapets, added corners, and ridgeline to reduce the wind impact on the roof. The validation shows that both the mean and RMS of the pressure coefficients are in good agreement with the wind tunnel results. The research results suggest that parapets with 500 mm height located at the corner and edges of complex roof geometry can effectively reduce extreme corner suction by 29% and roof uplift by 5.6%.
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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 | 0.000 | 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) | 0.000 | 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 it