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Record W2331410603 · doi:10.1061/9780784412367.189

Dynamic Behaviour and Performance Evaluation of Tuned Liquid Dampers (TLDs) Using Real-Time Hybrid Simulation

2012· article· en· W2331410603 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

VenueStructures Congress 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThermoluminescent dosimeterComputer scienceDamperSimulationMATLABDynamic simulationAutomotive engineeringStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Tuned liquid dampers (TLDs) are cost effective and low-maintenance dynamic vibration absorbers that are being used in flexible and lightly damped structures. A TLD is a passive control device that dissipates energy through the liquid boundary layer friction, the free surface contamination, and wave breaking. In this paper the dynamic behaviour and performance of TLDs are investigated through a series of the real time hybrid simulations on a steel frame equipped with a TLD on the roof level. The tests were carried out using a Quanser shaking table, and by implementing the real-time hybrid simulation algorithms in MATLAB Simulink, Real-time Workshop and WINCON. By combining computer simulation with physical testing, the real-time hybrid simulation method provides the response of the structure-TLD system by only testing the TLD. This way, the structure-TLD interaction will be captured without testing a scaled-down model of the entire system in the laboratory. By better understanding the TLD behaviour and its interaction with the structure, the results presented here will contribute to the improved design of the TLDs which will in turn result in the wider application of these devices.

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 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 score0.091
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.281
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