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Record W2087657330 · doi:10.1002/fld.2109

On the impact of anisotropic mesh adaptation on computational wind engineering

2009· article· en· W2087657330 on OpenAlex
Martin S. Aubé, Wagdi G. Habashi, Hongzhi Wang, Dennis Torok

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputational fluid dynamicsTurbulenceComputationGridSolverCube (algebra)Flow (mathematics)CFD in buildingsTowerEngineeringMathematicsComputer scienceMeteorologyAerospace engineeringMathematical optimizationPhysicsGeometryStructural engineeringAlgorithm

Abstract

fetched live from OpenAlex

Abstract This paper addresses the critical issue of the accuracy of CFD predictions for wind engineering. Flows around the Silsoe Cube, a high‐rise building (the Jin Mao Tower), and a low‐rise large‐span building (the Pudong International Airport) are computed with the Navier–Stokes solver FENSAP and compared with measurements. Computations are carried out for two wind directions, by solving the steady‐state ensemble‐averaged Navier–Stokes equations with the Spalart–Allmaras one‐equation turbulence model. Pressure coefficients compare well with wind tunnel experiments and the accuracy of the flow solutions is further improved via an automatic mesh adaptation that dynamically places grid points where the flow physics require them, while keeping the number of unknowns and solution time substantially at the same level. Copyright © 2009 John Wiley & Sons, Ltd.

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.

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)0.001
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: none
Teacher disagreement score0.602
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.032
GPT teacher head0.374
Teacher spread0.341 · 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