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Record W2936538166 · doi:10.1680/jencm.18.00024

Downwind flow behaviours of cuboid-shaped obstacles: modelling and experiments

2019· article· en· W2936538166 on OpenAlexfundno aff
Karl An, J. C. R. Hunt, Jimmy Chi Hung Fung

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Engineering and Computational Mechanics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersUniversity of Hong KongHong Kong University of Science and TechnologyCanada Excellence Research Chairs, Government of Canada
KeywordsCuboidComputational fluid dynamicsWakeMechanicsComputationTurbulenceInflowRobustness (evolution)Perturbation (astronomy)Environmental scienceComputer sciencePhysicsMathematicsGeometryAlgorithm

Abstract

fetched live from OpenAlex

Buildings or fence-like structures are frequently modelled as cuboids in simulations of environmental assessments. Understanding flows around and downwind of typical isolated buildings, in the form of cuboids, provide insightful building disposition strategies, which are particularly useful in the field of urban planning. Fast analytical models or computational numerical models are essential for sensitivity studies of various design parameters that require validation against field/experimental data. The aim of this study is to compare in detail the robustness of analytical perturbation models and the computational fluid dynamics (CFD) k − ε turbulence models for predicting the mean wake velocity defects along the centre line downwind of the cuboids, both in the near- and far-wake regions. Depending on aspect ratios of cuboids, the decay rate of maximum velocity defect behaves differently. Moreover, the CFD code over-predicts the magnitude of maximum defect in comparison with the perturbation models, whereas it over-predicts or under-predicts when compared to actual measurements. A physical explanation has been proposed in this paper. The steady CFD models, requiring less empirical input, are shown to provide satisfactory results for approximate computations of flows in the near- and far-wake regions of cuboid buildings with appropriate settings of inflow profiles and wall function.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.187
Teacher spread0.179 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations2
Published2019
Admission routes1
Has abstractyes

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