\tContributions Towards a Large Eddy Simulation Best Practice Guide for the Numerical Prediction of Wind Loads on Tall Buildings
Bibliographic record
Abstract
The influence of the domain height and width, and mesh and time step size on the results of a LES (Large-Eddy Simulation) to estimate wind loads on tall buildings was investigated. The results shall contribute to a future LES BPG (Best Practice Guide). Therefore, the wind flow around the CAARC Standard Tall Building, a well-established benchmark, was simulated with the software package Star-CCM+. The CDRFG (Consistent Discrete Random Flow Gener- ation Technique) was used to generate a turbulent inflow. The obtained results showed a good agreement with measurements from the BLWT (Boundary Layer Wind Tunnel) at the University of Western Ontario, Canada. Thus, the results indicated that the well-known rec- ommendations regarding the height and width of the computational domain could be reduced for LES. Moreover, potential fields for further research in the field of wind load estimation with LES were detected. For example, the choice of the most suitable selection of mesh sizes throughout the domain to obtain reasonable results and maintain an acceptable running time of the simulation.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.050 |
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".