The application of the quasi-steady vector model to low-rise buildings with sloped roofs
Bibliographic record
Abstract
This paper examines the performance of the quasi steady (QS) vector models on low-rise building roofs of different shapes and slopes. Wind tunnel tests were conducted for low-rise building models with gable and hip roofs of different slopes and compared with data from a flat roof building, for which the QS vector model is known to be accurate. It is observed that the performance of the QS vector model for sloped roofs on low-rise buildings depends on the details of the local flow field and aerodynamics. These models have good accuracy for regions with flow separation but are much less accurate for regions with attached or reattached flow. For regions on sloped roofs with flow separations, the QS vector model is less accurate than for the equivalent separated flow (leading edge) regions on flat roofs when there is separation at the eaves but has a similar level of performance when there is no leading edge separation on the windward roof prior to the separation at the ridge. This is because the separated flow over the leeward portions of the gable and hip roofs is disrupted by the developing windward roof boundary layers when the flow separates at the eaves. Velocity measurements positioned one roof height above the ridge are effective to conduct quasi-steady analysis for the leeward faces of gable and hip roofs where flow separation controls the local aerodynamics. This single probe location is less effective for windward roof faces, which would require different methods to capture the QS fluctuations associated with the developing roof boundary layers.
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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 | 0.000 | 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 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".