Computational Fluid Dynamics Validation of Rooftop Wind Regime in Complex Urban Environment
Bibliographic record
Abstract
This thesis presents a validation methodology for Computational Fluid Dynamics (CFD) assessments of rooftop wind regime in urban environments. A case study is carried out at the Donadeo Innovation Centre for Engineering (DICE) building at the University of Alberta campus. A numerical assessment of rooftop wind regime around buildings of the University of Alberta North campus has been performed by using 3D steady Reynolds-averaged Navier-Stokes equations, on a large-scale high-resolution grid using the ANSYS CFX. Four anemometers were set up on the roof of the DICE building and the computer software LabVIEW was used for data acquisition. Six months of data were collected and analyzed using different methods of data extraction and comparison. Four in-flow directions and three free-stream wind speeds were simulated, and the results of the assessment showed that the CFD simulation results are much more sensitive to upstream geometry modelling, when compared to downstream, even in cases of areas of low roughness lengths. A wind resource assessment was also conducted, and the effect of averaging period on the average wind power density and turbulence intensity was studied. It was found that changing averaging period does not influence the average wind power density, but the turbulence intensity decreased with decreasing averaging period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".