Computational wind engineering: 30 years of research progress in building structures and environment
Bibliographic record
Abstract
The paper reviews the evolution of computational wind engineering from environmental and structural perspectives, since the inaugural conference of computational wind engineering held in Tokyo 30 years ago (CWE 92). The progress in computational methodologies and important aspects for accurate analysis are discussed. As a groundwork for the application of computational fluid dynamics (CFD) to various environmental issues , the importance of accurate modeling of atmospheric boundary layer, urban boundary layer, and urban canopy layer is pronounced. Environmental applications refer to urban micro-climate, pedestrian level wind, near-field pollutant dispersion , natural and urban ventilation, urban wind energy and snow/sand erosion and accumulation. Structural applications refer to wind loading on low- and high-rise buildings, including wind directionality . The most seminal contributions are examined, and their results are presented. It becomes clear that the engineering community has gained more benefits from environmental than structural computational wind engineering applications , mainly due to the usually less demanding computational needs for the former. Future challenges in CFD applications are thoroughly discussed and the need to bridge the gap between environmental and structural applications is highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".