Requirements for partial turbulence simulations using nondimensional turbulence energy contributions
Bibliographic record
Abstract
Quantifying turbulence effects is crucial for understanding building aerodynamics and for ensuring accurate wind tunnel test methods. This is especially important in wind tunnel methods that require post-experiment adjustments because approximate wind fields are used, such as the Partial Turbulence Simulation (PTS) approach. Understanding and analyzing these effects enables load adjustments since the PTS method only requires matching the high frequency portions of the upstream spectra of the longitudinal velocity component in model and full-scale. However, the limits for which the PTS method is applicable are unclear in terms of the allowable range of wind field characteristics that can be used in the wind tunnel simulation. To address this, the paper utilizes two nondimensional parameters, one representing the small-scale turbulence energy, E S , and the other the large-scale turbulence energy, E L , to elaborate the aerodynamic effects of turbulence intensity and integral length scales in the upstream wind. The results show that the maximum allowable mismatch ratio of integral length scales and of Jensen numbers between model and full-scale simulations depend on the target small-scale turbulence energy and the maximum allowable deviation of small-scale energy. By quantifying the effects of E S and E L on area-averaged pressure coefficients, the allowable limits are identified for wind tunnel test parameters that lead to negligible differences in the resultant pressure coefficient statistics in regions of separated-reattaching flow on the roof of a low-rise building.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".