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Record W2042183768 · doi:10.1177/1077546314567524

Numerical modeling of dynamic behavior of annular tuned liquid dampers for the application in wind towers under seismic loading

2015· article· en· W2042183768 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Vibration and Control · 2015
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersMitacs
KeywordsTowerTurbineFinite element methodVibrationAmplitudeGeologyDamperStructural engineeringFrequency domainMechanicsSeismologyPhysicsAcousticsEngineeringComputer science

Abstract

fetched live from OpenAlex

In this study, an innovative technique is introduced for application of annular tuned liquid dampers (ATLD) in wind turbines subjected to seismic ground motions. The performance of ATLD in mitigating the vibration of wind turbines is investigated using numerical simulations. The wind tower is modeled using finite element method while the fluid domain is simulated by finite volume method. The numerical study considers the dynamic behavior of ATLD under different seismic records. Also, the effects of earthquake amplitude and frequency content, structural damping and detuning on the interaction between the tower and ATLD are investigated. The results of time-history analysis show that the ATLD is effective in mitigating the response of a wind turbine when subjected to large-amplitude seismic loading. The wind tower equipped with the proposed ATLD also behaves in the elastic range of response during the considered earthquake records which is critical for the integrity and safety of these structures.

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: none
Teacher disagreement score0.753
Threshold uncertainty score0.201

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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