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Record W7116121633 · doi:10.82417/3yht-jb50

Searching for key variables influencing upstream building effects in urban CFD simulations

2025· other· en· W7116121633 on OpenAlexfundaboutno aff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersAgencia Nacional de Investigación y DesarrolloUniversity of Alberta
KeywordsUpstream (networking)Computational fluid dynamicsRoofKey (lock)Wind tunnelWind speedField (mathematics)CFD in buildingsUrban planning

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.328
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.290
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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