Numerical Investigation of the Built Urban Environment Immersed in Atmospheric Boundary Layer
Bibliographic record
Abstract
This thesis focuses on numerical investigation of the built-environment immersed in atmospheric boundary layer using Computational Fluid Dynamics (CFD). The thesis has two parts. In part 1, Pedestrian Level Wind (PLW) maps were developed for downtown Toronto using a novel CFD model linked with Meteorology data at Toronto airport weather stations. This resulted in real-time and statistical wind speeds at pedestrian level. Resulting wind speeds were validated using measurements from a local weather station at University of Toronto. In part 2, a new inflow turbulence generator was developed to obtain accurate aerodynamic forces for tall buildings using Large Eddy Simulation. The model relies on calibrating an existing inflow generator to improve its accuracy. The resulting model yielded average of 10% lower error for the dynamic forces compared with the uncalibrated model. The presented CFD models in parts 1 and 2 lead to accurate wind engineering applications in the built-environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".