MétaCan
Menu
Back to cohort
Record W3133856383 · doi:10.2749/newyork.2019.2664

Flutter Analysis Using Quasi-Steady Time-Domain Flutter Derivatives

2019· article· en· W3133856383 on OpenAlex

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

VenueReport · 2019
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversité de SherbrookeWestern University
Fundersnot available
KeywordsFlutterAccelerationTime domainVibrationDeckNonlinear systemStructural engineeringBridge (graph theory)MechanicsComputer scienceEngineeringAcousticsPhysicsAerodynamicsClassical mechanics

Abstract

fetched live from OpenAlex

<p>To be able to perform nonlinear flutter analyses for bridges, time‐domain approaches should be used instead of Scanlan’s formulation of self‐excited forces. Thus, this paper addresses the development and validation of a modified quasi‐steady time‐domain model similar to Scanlan’s approach that is based on the velocity and acceleration of the bridge deck. In this formulation, quasi‐steady time‐domain flutter derivatives measured in the wind tunnel through forced‐vibration tests at absolute constant velocity and acceleration are used. For this, a unique test rig, which can be used either for free‐ or forced‐vibration tests, was utilized. By measuring the time‐domain flutter derivatives of the Great Belt Bridge, their nondimensionalization with respect to the bridge‐deck width, velocity and acceleration of the deck is validated. Then, time‐domain flutter analyses are performed using this new model. They agree with the experimental critical speed and the prediction using Scanlan’s model.</p>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.380
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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