Effects of the Turbulence Integral Scale on the Non-Gaussian Properties and Extreme Wind Loads of Surface Pressure on a CAARC Model
Bibliographic record
Abstract
To study the effects of the turbulence integral scale on the non-Gaussian properties and extreme wind loads of surface pressure, the surface pressures for two Commonwealth Advisory Aeronautical Research Council (CAARC) scaled models were measured in three turbulent flow fields with different turbulence integral scales. The results show that the surface pressure distribution on the windward surface is fundamentally Gaussian, while the surface pressures on the side and leeward surfaces are markedly non-Gaussian. The deviation from normality strongly depends on the ratio of the turbulence integral scale to the windward width (Lux/D). With changing Lux/D, the fluctuating pressure, skewness, kurtosis, probability density distribution, non-Gaussian peak factors, and extreme wind loads vary significantly. In addition, the surface pressure nonnormality becomes more evident for lower Lux/D wind fields, increasing Sk, Ku, and the fluctuating pressure’s peak factor. In contrast, the fluctuating pressure decreases with decreasing wind-field Lux/D, resulting in the underestimation of extreme wind loads. Further, the extreme wind load maximal error margin reaches 30.7% when the simulated turbulence integral scale error margin is 70%, even for nonnormal surface pressures. Hence, nonnormality of the surface pressure and the effects of the turbulence integral scale should be carefully considered when estimating extreme wind loads for CAARC standard tall buildings using wind-tunnel tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".