CFD simulation of thunderstorm outflow and atmospheric boundary layer winds interactions in urban canyons: Validation and flow dynamics
Bibliographic record
Abstract
Thunderstorm outflows generate intense near-ground winds that pose significant risks to urban infrastructure. However, outflow wind actions are not included in structural design codes because their interactions with urban environments are not yet fully understood. This study employs Computational Fluid Dynamics (CFD) to simulate outflow propagation in urban canyons under the influence of background atmospheric boundary layer (ABL) winds. Wind tunnel measurements are used to validate the CFD results, with a focus on the performance of Reynolds-Averaged Navier–Stokes turbulence models. The low-Reynolds k − ε model, combined with a highly refined mesh ( y + <5), effectively captures near-wall flow dynamics and accurately replicates experimental data. CFD reveals critical flow details at the surface, where outflow peaks, and at rooftop levels—areas that are often inaccessible to physical instruments. Interestingly, peak velocities in weak ABL wind conditions are significantly higher than in stronger ABL wind cases, highlighting nonlinear interactions between ABL and outflow winds. Compared to open terrain (i.e., no canyon), low-rise (15 m) and high-rise (50 m) urban canyons increase peak near-ground wind velocities by over 70 % and 130 %, respectively, under a weak ABL wind condition. The canyon geometry confines the outflow by preventing its radial spread and, therefore, maintains high velocities over greater vertical and radial distances. By complementing experimental studies, CFD offers valuable insights into urban wind behavior and supports efforts to enhance wind resilience in cities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".