Numerical characterization of downburst wind field at WindEEE dome
Bibliographic record
Abstract
Downbursts are acknowledged for being a major loading hazard for horizontally-extending structures like transmission line systems. With these structures being inherently flexible, it is important to characterize the turbulence associated with the wind flow of downburst events being essential to quantify dynamic excitations on structures. Accordingly, the current study numerically characterizes the downburst wind field of open terrain simulated at the Wind Engineering, Energy and Environment (WindEEE) dome testing facility at The University of Western Ontario in Canada through a high-resolution large eddy simulation (LES). The study validates the numerical simulation considering both the mean and the turbulent components of the flow. It then provides a detailed visual description of the flow at WindEEE through the capabilities enabled by LES to identify the key factors affecting the flow. The study also presents the spatial distribution of turbulence intensities and length scales computed from the numerical model and compares them with previous values reported in the literature. The comparison shows the ability of the downburst simulated at WindEEE to reproduce turbulence characteristics similar to those reported from field measurements. The study also indicates that downburst turbulence is well-correlated circumferentially which imposes high correlated loads on horizontally-distributed structures such as transmission lines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".