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Record W4323537976 · doi:10.1139/cjce-2020-0050

Comparison of the empirical formula and the “CFD”-based semi-empirical method in the prediction of the critical speed of flutter

2023· article· en· W4323537976 on OpenAlexvenueno aff
Essam Abdulsattar, Mohamed A. Abdel–Naby, M. A. El-Naggar

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFlutterComputational fluid dynamicsWind speedCritical speedDeckWind tunnelStructural engineeringMathematicsSection (typography)Empirical modellingSpan (engineering)AerodynamicsMechanicsEngineeringGeologyRotor (electric)Computer scienceSimulationPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, the critical wind speed of flutter has been predicted by using two-dimensional computational fluid dynamics (CFD) model side by side with the empirical formula (Selberg's formula, 1961). The deck section of the Great Belt Bridge's main span has been used herein as a reference among other sectional varieties. For each case of the examined sections, the width was changed case by case, while its depth maintained unchanged. Both of the two methods, the semi-empirical using the “CFD” simulation and the empirical formula have been employed to predict the critical wind speed of flutter for a variety of the (width/depth) ratio and the natural frequency ratio (torsional/vertical). The results were obtained from the semi-empirical method and validated by comparison with wind tunnel test for typical section model of the Great Belt Bridge presented in the literature, and also compared with the empirical method. The results were represented graphically for each method and they have indicated the variation of the critical speed in terms of the section dimension and its natural frequencies, and furthermore, the change of the difference between the two methods was represented too.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.303
Teacher spread0.261 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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