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Record W1974939693 · doi:10.1002/eqe.452

An investigation of tuned liquid dampers equipped with damping screens under 2D excitation

2005· article· en· W1974939693 on OpenAlexafffund
Michael J. Tait, Ashraf A. El Damatty, N. Isyumov

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2005
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsWestern UniversityMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlosh dynamicsEarthquake shaking tableDamperPerpendicularVibrationStructural engineeringMechanicsFlow (mathematics)EngineeringPhysicsAcousticsGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract This paper reports on the results of a study conducted on tanks partially filled with water, representing tuned liquid dampers (TLD), subjected to both 1D and 2D horizontal excitations. The sloshing response of the water in the tank is characterized by the free surface motion, the resulting base shear force, and evaluation of the energy dissipated by the sloshing water. A 1D non‐linear flow model capable of simulating a TLD equipped with damping screens is employed to model a 2D TLD. Application of this particular model requires the assumption that the response is decoupled and can be treated as the summation of two independent 1D TLDs. Results from the non‐linear flow model are compared with the 2D experimental shake table test results leading to a validation of the decoupled response assumption. This attractive decoupled response property allows square and rectangular tanks to be used as 2D TLDs, which can simultaneously reduce the dynamic response of a structure in two perpendicular modes of vibration. Copyright © 2005 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.214
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2005
Admission routes2
Has abstractyes

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