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Thermal-Mechanical Model for Predicting the Wind and Seismic Response of Viscoelastic Dampers

2016· article· en· W2410949968 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

VenueJournal of Engineering Mechanics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDamperViscoelasticityStructural engineeringThermalSeismic loadingTerm (time)Dynamic loadingMaterials scienceMechanicsEngineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

This paper presents a simplified coupled thermal-mechanical model for the modelling of viscoelastic dampers with temperature- and frequency-dependent properties subjected to long-term and short-term dynamic loading due to wind and earthquakes. In this numerical model, the self-heating effect caused by molecular-level friction in the viscoelastic (VE) material is captured explicitly using a finite volume thermal diffusion model based only on the physical properties of the steel and VE material. The thermal model is coupled to a single degree of freedom (SDOF) mechanical model, which produces an efficient computational scheme for time-history analyses of VE dampers under both long-term and short-term loading scenarios. The predictions from the proposed model are compared with full-scale experimental results of long-duration wind loading ranging from design level events to extremely rare events, and for shorter but more intense seismic loading scenarios. It is shown that results obtained using this proposed simplified model with idealized geometry predicted well the dynamic response of the damper for all ranges of experimental results thus validating a robust physics based tool for predicting the response of VE dampers.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.186

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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