Searching for key variables influencing upstream building effects in urban CFD simulations
Bibliographic record
Abstract
In this study, we demonstrate that upstream buildings exert a significant influence on the wind conditions at rooftop levels for energy generation. By systematically removing, adding, and modifying the layout of a section of the University of Alberta North Campus, we conclude that the obstructive effect of upstream buildings is a cumulative phenomenon. This finding adds complexity to urban wind energy assessments that are based on city morphology.A series of CFD simulations, comparing various building modifications (including the removal of tall obstructions, sheltered structures, and alterations to roof shape) revealed that changes in building inclusion can lead to substantial variations in both wind velocity and energy yields. These variations underscore the importance of understanding the complex interactions between urban structures and the wind.Following an initial exploration of building interactions, we are performing a systematic CFD simulations aimed at identifying the key variables that govern this cumulative influence. The objective is to characterize how factors such as friction velocity, surface roughness, building width, and relative distance (normalized by a characteristic height difference) affect urban wind flow. The results of this investigation are intended to guide the appropriate inclusion of upstream buildings in CFD simulations, and they highlight the need for further validation through wind tunnel experiments and field measurements.
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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 | 0.000 | 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".