A Comparative Study of the Use of Experimental and Computational Techniques to Assess Turbulence Above the FATO to Support the Design and Operations of Vertiports in the Built Environment
Bibliographic record
Abstract
Turbulence conditions at hospital heliports in the built environment are routinely assessed at the design stage through experimental, physical testing in boundary layer wind tunnels. Wind tunnel testing is the gold standard to evaluate wind conditions on and around buildings where human safety is of the upmost concern. Numerical techniques, such as computation fluid dynamics (CFD) are continuously improving and may offer a viable alternative to wind tunnel testing in some cases. Within the CFD toolbox, there are several techniques to simulate a flow field in an urban or suburban context. These techniques have advantages and disadvantages in terms of ease of use, efficiency, costs, level of fidelity, and reliability. This paper compares high-fidelity CFD tools to wind tunnel testing for two vertiport case studies in different urban settings with different wind climates. The results of this research inform the selection of the right tool to support vertiport design and operations and to protect public safety.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".