Bibliographic record
Abstract
Numerical (CFD) and physical (laboratory) experiments were conducted in parallel to simulate downburst and tornado-like flow fields in order to determine the effects of these thunderstorm winds on buildings and structures. The physical experiments served to: (i) benchmark the CFD results and (ii) to help in designing the next generation of downburst and tornado simulators in order to physically test scaled structural models. Downburst jet-like simulations showed the complex vortex structure of these winds for which the maximum velocity happens very close to the surface. Above a certain critical Reynolds number the downburst flow is rather independent of length or velocity scaling and only dependent on the terrain roughness. This allows the scaling of numerical or laboratory downburst-like experiments to full scale phenomena. The flow field was then applied to estimate steady-state responses of tall buildings and it was determined that in certain conditions the downburst winds may become dominant when compared to synoptic, boundary layer winds. Tornado-like simulations showed that the wind field is highly dependent on the swirl ratio. Several swirl ratios have been investigated both numerically and experimentally and results compared well. Moreover, by matching high swirl ratio numerical results with full scale Doppler radar measurements a preliminary relation has been established between the swirl and the Fujita scale.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".