Assessment of wind-driven rain (WDR) on buildings in an urban area: comparison of different CFD frameworks
Bibliographic record
Abstract
Abstract The study of Wind-Driven Rain (WDR) loading on building facades is essential to design more sustainable and climate-resilient buildings, as well as to prevent further damage to old and historical buildings. Both WDR loading on buildings and façade responses to impinging raindrops have been studied previously but results for such a multi-parameter problem are not generally conclusive. Thus, the relevant provisions of ISO semi-empirical model cannot be applied with confidence for complex building configurations, such as those in urban areas since the WDR prediction can be more than twice that of the experimental data. In this paper, the Eulerian Multiphase (ME) technique is coupled with the RANS model to simulate the WDR loading on a six-story building under steady rainfall event conditions. Wind and WDR results are compared with the available wind-tunnel and on-site field measurement results, respectively. The field measurements were carried out on a six-story mid-rise residential building, located in Vancouver, Canada. The results show that the Euler-Euler framework (RANS-EM) predicts wind and WDR in such an urban area configuration more rapidly and accurately compared to the more traditional Euler-Lagrange framework (RANS-LPT) for both stand-alone and urban area configurations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".