Flow Over Model Buildings With Sloped Roofs
Bibliographic record
Abstract
The flow field around model buildings with different sloped roofs was investigated using Particle Image Velocimetry (PIV). The flow around model buildings having flat roofs was studied by many authors. Although buildings with sloped roofs are the most common type of low rise buildings, the flow around these buildings are not well known. Most of the studies for these types of buildings were made for the determination of surface pressures. The aim of this study is to highlight the fundamental differences between flat roofs and sloped roofs for three-dimensional obstacle flows. The experiments were performed in a wind tunnel having a cross section of 300 mm × 400 mm. All the models were 30 mm high (vertical wall) and were placed in a thin turbulent boundary layer. Three Reynolds numbers, based on the height of the obstacle, were used (12000, 22 000, 32 000). Furthermore, the quantitative data is analyzed and statistical results describing the mean and fluctuating velocity fields are presented. Finally, the surface pressures on the median plane were studied in order to correlate these pressures with the flow topology of different sloped roofs. It was found that upstream of the obstacle, the flow topology for the model having sloped roofs was similar to that of a flat roof apart from an increase in size of the well-known horseshoe vortex. However, the flow topology is not the same over different roofs, on the sides of the models and immediately downstream of the models. For the Reynolds number studied, there are no coherent flow structures over the upstream sloped roofs while an arch vortex is created on the sides of the models. This arch vortex is similar to the arch vortex that is created over a flat roof. An arch vortex is also present downstream of the models. The lower part of this vortex is similar to the one created for a flat roof. However, the upper part of the arch vortex starts from the tip of the roof and continues downstream and has an ellipse shape. This vortex also increases in size with the slope of the roof.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".