CFD investigation of wind turbulence on 2D square and angle section
Bibliographic record
Abstract
Aerodynamic forces due to air flow are a crucial and intrinsic part of analysis and design for modern structures of various uses. Dynamic wind forces acting on the structure can be predicted during the planning and design phase either experimentally, by carrying out the experiments on its scaled-down prototype in a wind tunnel, or computationally, by numerical simulation of air flow around the structure using mathematical models that explain the fluid flow. The National Building Code of Canada (NBC) contains guidelines to determine the wind load on various types and shapes of structural elements, using a net force formula that takes into account the dynamic effects of the wind. The guidelines are based on wind tunnel experiments but according to the NBC commentaries, realistic wind velocity profiles and turbulence were not simulated in those experiments that were conducted many decades ago, for the most part. Due to the shortcomings of the experimental data, the code mentions that the proposed calculation method should be used with caution. Using the latest computational technologies available, the accuracy of the data can be validated. Since it is difficult and expensive to carry out wind tunnel tests systematically on various structural shapes covered in NBC, the computational approach offers a good alternative to revisit these wind effect predictions on simple shapes. Such a computational study is conducted here where two cross-sectional shapes, a square and an equal leg angle shape, are modeled in both laminar and turbulent wind flows using a commercial fluid dynamics finite element software. A computational parametric study was carried out for the chosen Spalart Allmaras turbulent model on angle sections to study the effects of turbulence intensity, angle of attack and time step on the results
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".