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Quality assessment of peak pressure estimates in high-rise building using large eddy simulation

2025· article· en· W4413428850 on OpenAlexaff
Pedro M. Brito, Almerindo D. Ferreira, António C.M. Sousa

Bibliographic record

VenueBuilding and Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of New Brunswick
FundersFundação para a Ciência e a Tecnologia
KeywordsEnvironmental scienceQuality (philosophy)MeteorologyGeographyPhysics

Abstract

fetched live from OpenAlex

• Quantifies numerical and modelling errors in LES-predicted peak pressures. • Applies systematic grid and model variation to map error estimates. • Uses 96-cells-per-breadth resolution to achieve low grid-induced uncertainty. • Shows grid-resolution sensitivity exceeds that of the sub-grid-scale model constant. • Confirms 95 % of peak pressures match wind-tunnel data within ±30 % tolerance. Analysis of peak surface pressures is essential for specifying wind-resistant cladding of tall buildings. Large eddy simulation (LES) holds promise for predicting peak wind action, yet its reliability is challenged by uncertainties arising from extreme value analysis and grid-controlled scale filtering. This study postulates that quantifying numerical and modelling errors in the LES pressure solutions improves design reliability and guides grid resolution specifications for high-fidelity peak pressure prediction. Thus, the key objectives were to ( i ) map magnitudes of numerical (truncation) and modelling errors along facades—achieved here through the novel application of the systematic grid and model variation method—and ( ii ) assess the accuracy of low-uncertainty peak pressure solutions against experimental measurements. Simulations reproduced a benchmark from the Tokyo Polytechnic University, modelling a square-based building with height-to-breadth ratio of 5:1 subjected to orthogonal and oblique wind incidences. Peak pressure coefficients at 500 locations were extracted from a Gumbel distribution of extremes, recorded during 30 min of equivalent full-scale exposure to 100-year return wind speeds. Using an isotropic wall-tangent resolution of 96 cells per building breadth, the LES peak pressure results showed generally low uncertainty, with area-averaged numerical and modelling errors of 7.3 % and 4.1 %, respectively. This configuration demonstrated respectable accuracy: 55 %, 85 %, and 95 % of peak pressure predictions fell within ±10 %, ±20 %, and ±30 % of the experimental references. Ultimately, this work presents a systematic LES framework for reliable peak pressure prediction, well-suited for third-party reproduction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.316
Teacher spread0.297 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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