Characteristics of street canyon flow using large-eddy simulation with a drag-porosity modelled atmospheric boundary layer
Bibliographic record
Abstract
Turbulence and turbulent structures are key characteristics of atmospheric boundary layer flows, playing a crucial role in momentum and energy transport. The presence of urban roughness significantly influences turbulence generation and modulation, as large-scale atmospheric turbulent structures interact with smaller-scale, canopy-induced structures.The traditional way to solve the near-wall region of the obstacle in computational fluid dynamics simulations of urban canopy flows requires large computational cost. Research shows that an additional sink term of the drag force varying with height added to the momentum equation can serve to reproduce the key features in high Reynolds number boundary layer flows. Hence, the drag-porosity model is explored in the present work to simulate a realistic unsteady, neutral, Coriolis-free atmospheric boundary layer developing over the urban canopy implicitly using Large-Eddy Simulation (LES) in OpenFOAM v2406. A comprehensive analysis of one-point turbulence statistics, two-dimensional energy spectra, two-point correlation functions, and the interaction between the most energetic scales identifies characteristic features commonly observed in wall-bounded turbulent flows. Although the drag-porosity model does not capture the flow structure within the canopy, because the obstacles are not explicitly represented, it is efficient and low-cost when used as a precursor simulation, with satisfying accuracy for successive obstacle-resolving LES.The street-canyon configuration is a simple model to study airflow in nearby city streets, helping us understand street-scale ventilation and air quality, which affect pedestrian-level comfort and air quality. To further investigate street canyon flows, a successor simulation is conducted on a street canyon, represented as two bars with a canyon height-to-width ratio of 1, within a staggered cube array having a 25% plan area packing density, representing urban roughness. Using inlet conditions from the above-mentioned precursor simulation based on the drag-porosity approach, the setup includes two rows of staggered cubes downstream of the inlet, followed by a street canyon to allow full flow development. Downstream of the canyon, another two rows of staggered cubes and a drag-porous region are introduced before the outlet to minimize numerical instabilities that could propagate upstream, potentially compromising accuracy or causing the simulation to terminate. The full presentation will include a comprehensive analysis of one-point turbulence statistics, integral length scales, and two-point correlations are compared with previously obtained experimental results, in the wall-normal direction and at the roof level. The street canyon simulation expands the dataset for street canyon studies and serves as a crucial step toward the development of reduced-order models.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".