Experimentally estimating wind load coefficients for tornadoes – An alternative perspective
Bibliographic record
Abstract
Given the increased interest in tornado-induced wind loading (in part exhibited by inclusion of such loading in wind standards around the world) it is vital to understand the various characteristics of such loading and their relative overall importance. As such, employing a range of types of simulations and simulators would be helpful for understanding and estimating tornado-induced wind load coefficients. This paper advocates a quasi-steady framework to estimate tornado-induced wind loads and identifies the tornado flow characteristics most likely to influence these loads. The flow characteristics discussed include the flow field itself, the static pressure field, vortex translation, flow turbulence, streamline curvature, and flow acceleration. A fundamental conclusion of this paper is that pressure coefficients for tornado-induced wind loading should always be measured and reported as functions of these characteristics. The paper discusses various alternatives for simulating these characteristics and highlights which types of facilities would be effective for studying each one. Such approaches will also require deliberately measuring velocity and static pressure simultaneously with any load measurements made on a building model. This will then help clarify the dependencies of pressure and load coefficients to the various parameters, will limit the parameter space that must be explored to understand extreme loading, and will facilitate easier comparison among different laboratory results, all of which will ultimately lead to improved design standards.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".