Evaluating the wind loads on high-rise buildings of various plan dimensions through numerical simulations
Bibliographic record
Abstract
With the increasing number of high-rise buildings being constructed, there is a growing emphasis on enhancing their aerodynamic performance. Computational Fluid Dynamics (CFD) has emerged as a valuable tool for investigating wind loading around these structures due to its cost-effectiveness and improved spatial resolution. The objective of this study is to assess the capability of CFD methods in capturing mean, fluctuating, and area-averaged pressure coefficients, by comparing CFD results with Wind Tunnel (WT) data on high rise buildings with various plan dimensions, which are rarely documented in previous CFD studies. Numerical simulations were conducted on six rectangular high-rise buildings with varying plan dimensions and inflow angles using two techniques: the Wall Modeled Large Eddy Simulation (WMLES) with the Smagorinsky model and Reynolds-Averaged Navier-Stokes (RANS) with the k-ω SST turbulence model. For accurate WMLES simulations, a turbulence inlet was generated using a divergence-free synthetic turbulence generator and validated against available WT data. Velocity streamlines and pressure contours were presented and analyzed as part of the investigation. The results demonstrated that WMLES exhibited promising outcomes in terms of local mean pressure coefficients, area-averaged mean pressure coefficients, and fluctuating pressure coefficients. On the other hand, RANS results displayed larger errors. Furthermore, the study observed that WMLES effectively captured the common flow characteristics around high-rise buildings, where the length-to-breadth ratio ( L/B ) significantly influenced the size of eddies formed behind the structures. • WMLES and steady RANS are used to evaluate forces on rectangular buildings. • WMLES shows good agreement but has larger errors in some regions. • RANS with k – ω SST model has larger error on back and side surfaces for some cases. • WMLES is overall more accurate than RANS in predicting wind loading.
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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".