Comparison of wind-driven rain load on building facades in the urban environment and open field: A case study on two buildings in Zurich, Switzerland
Bibliographic record
Abstract
Wind-driven rain (WDR) is one of the largest moisture sources affecting the hygrothermal performance and durability of building envelopes. Accurate estimation of WDR loads on building facades as boundary condition is important for the hygrothermal analysis of building envelopes. In this work, computational fluid dynamics (CFD) simulations and semi-empirical models are used to compare WDR load on building facades in the urban environment and open field. The research focuses on two historical buildings in the city center of Zurich, Switzerland. There is a very large difference in terms of the local wind-flow fields around buildings in the urban environment and open field. Raindrop trajectories in the open field are more horizontal due to higher wind speeds around the building. In the urban environment, the wind speed is lower yielding droplets following trajectories that are more parallel to the facades. The building overhang is more effective at rain protection in the urban environment compared to the building in the open field due to a larger rain sheltering effect, since the rain trajectories are more parallel to the facades. There is a very large difference in WDR load and distribution on the facade of buildings in the urban environment and open field. WDR loads on buildings in the urban environment are much smaller than in the open field. When semi-empirical models are used for this case study, there is large uncertainty in the prediction and the predicted WDR load can be many times larger compared to the load predicted by CFD.
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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 | 0.001 | 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 | 0.001 |
| 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".