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Record W2147363536 · doi:10.1115/1.4007416

The Influence of Tank Orientation Angle on a 2D Structure-Tuned Liquid Damper System

2013· article· en· W2147363536 on OpenAlexaff
J.S. Love, Michael J. Tait

Bibliographic record

VenueJournal of vibration and acoustics · 2013
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsMcMaster UniversityRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsSlosh dynamicsOrientation (vector space)Thermoluminescent dosimeterMechanicsFluid–structure interactionDamperPrincipal axis theoremStructural engineeringVibrationPhysicsMaterials scienceAcousticsOpticsEngineeringGeometryMathematicsFinite element method

Abstract

fetched live from OpenAlex

Tuned liquid dampers (TLDs) utilize sloshing fluid to absorb and dissipate structural vibrational energy, thereby reducing wind induced dynamic motion. By selecting the appropriate tank length, width, and fluid depth, a rectangular TLD can control two structural sway modes simultaneously if the TLD tank is aligned with the principal axes of the structure. This study considers the influence of the TLD tank orientation on the behavior of a 2D structure-TLD system. The sloshing fluid is represented using a linearized equivalent mechanical model. The mechanical model is coupled to a 2D structure at an angle with respect to the principal axes of the structure. Equations of motion for the system are developed using Lagrange’s equation. If the TLD and structure are not aligned, the system responds as a coupled four degree of freedom system. The proposed model is validated by conducting structure-TLD system tests. The predicted and experimental structural displacements and fluid response are in agreement. An approximate method is developed to provide an initial estimate of the structural response based on an effective mass ratio. The results of this study show that for small TLD orientation angles, the performance of the TLD is insensitive to TLD orientation.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.160

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 head0.004
GPT teacher head0.193
Teacher spread0.190 · 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 teacher head, 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

Citations8
Published2013
Admission routes1
Has abstractyes

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