CFD simulation of the wind flow under lift-up buildings using a porous approach
Bibliographic record
Abstract
Modeling the urban climate using computational fluid dynamics (CFD) is essential for assessing urban thermal comfort and developing heat wave mitigation solutions. One mitigation strategy may rely on the enhancement of urban ventilation and the use of porous urban environments, as the lift-up, or on pilotis, buildings. Lift-up buildings have their ground floor in whole or in part supported by columns and shear walls, allowing wind flow through the building at pedestrian level. CFD modeling of these intricate geometries is computationally challenging for large urban neighborhoods. Simplifications, such as removing columns to obtain an acceptable computational cost, lead to erroneous results in the global flow pattern. To balance accuracy and computational cost, the impact of complex ground floor geometry on wind flow is modeled using a porosity sink term in the Navier-Stokes equations based on the Darcy-Forchheimer law. The improved CFD modeling of a simple lift-up building is validated with wind tunnel measurements. Simulations of wind flow around a realistic lift-up building for different wind directions determine the Forchheimer coefficients required for the porosity approach. Comparison of reference and numerical porosity results demonstrates a very good agreement in mean velocity patterns. The study demonstrates that oversimplifications, such as removing all columns, leads to an unacceptable overestimation of the wind velocity at pedestrian level. The paper highlights the benefits of the numerical porosity approach and its necessity for accurate urban-scale simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".