Modelling downburst velocity fields in relation to Main Wind Force Resisting Systems
Bibliographic record
Abstract
Over the past two decades, wind engineering has focused on non-synoptic wind storms, which exhibit greater spatio-temporal complexity than synoptic scale winds. Here we focus on the modelling of downburst velocity fields in relation to the way these models can be used to determine structural responses to downburst winds. Two approaches have been defined in relation to the Main Wind Force Resisting Systems (MWFRS): (i) the Gust Front Factor (GFF) from Professor Ashan Kareem’s group at Notre Dame University in USA and (ii) the Thunderstorm Response Spectrum Technique (TRST) from the late Professor Giovanni Solari’s team at the University of Genova in Italy. Both methods decompose the downburst mean wind field into a vertical profile and a time variation. Here we focus on the modelling of downburst velocity fields in terms of spatial and time characterizations including the effects of translation, atmospheric boundary layer (ABL) superposition and surface effects. Herein, we focus on analysing how various analytical models, that include both time and space velocity variations, can be adapted to fit an experimental database of downburst-like flows (DLFs), generated at WindEEE Dome at Western University under the project THUNDERR. The calibration is conducted through the superposition of stationary mean flow fields with the effects of translation, background synoptic wind and surface roughness. Analytical models for the profile variation, the Oseguera-Bowles-Vicroy (OBV) and Wood & Kwok models, along with the sine wave models for temporal variation, are tested against this extensive database. The resulting adapted proposed models provide a potential frame on DLFs to be applied in the context of MWFRS approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".