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Record W4237403642 · doi:10.32920/ryerson.14649891

Tornado dynamics study using immersed boundary (IB) - Lattice Boltzmann Method (LBM) on a 7-cylinder building configuration

2021· preprint· en· W4237403642 on OpenAlexafffund
Rangaraj Palanisamy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTornadoLattice Boltzmann methodsAerodynamicsCylinderMechanicsVortexImmersed boundary methodDrag coefficientPhysicsMeteorologyBoundary (topology)DragMathematicsGeometryMathematical analysis

Abstract

fetched live from OpenAlex

Tornadoes are disastrous, naturally occurring atmospheric phenomena; they cause fatalities; they damage properties with an exceptional combination of translational and rotational velocities. Despite many studies on tornado-structure interaction, the research papers on tornado-multi-body interactions are limited. This research studies the effects of a tornadic wind on a 7-cylinder building model at several orientations in 2-D using a powerful Immersed Boundary-Lattice Boltzmann Method (IB-LBM). The tornadic wind was simulated by a customized Rankine Combined Vortex Model (RCVM). The wind-loadings on the seven cylinders were quantified using aerodynamic force and moment coefficients. The essential flow features associated with a vortex-structure interaction was investigated in great detail by doing a case study. Then, a unique optimization procedure was utilized to detect individual safe zones for each aerodynamic coefficient. Finally, an overall safe zone for the complete 7-cylinder building model has been ascertained to be between 29° and 69° by analyzing the individual safe zones.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
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 score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.038
GPT teacher head0.342
Teacher spread0.303 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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