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Record W2050690789 · doi:10.1115/1.4023805

Nonlinear Dynamic Behavior of a Flexible Shaft Supported by Smart Hydrostatic Squeeze Film Dampers

2013· article· en· W2050690789 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 Tribology · 2013
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsElectrorheological fluidDamperHydrostatic equilibriumNonlinear systemVibrationSmart materialViscosityMagnetorheological fluidMaterials scienceElectric fieldForce dynamicsStructural engineeringMechanicsEngineeringMechanical engineeringComposite materialPhysicsAcoustics

Abstract

fetched live from OpenAlex

The aim of this research is to study the nonlinear dynamic behavior of a flexible shaft supported by smart hydrostatic squeeze film dampers, which are filled with a negative electrorheological fluid (NERF). A nonlinear model of the hydrostatic squeeze film damper has been developed in order to study the effect of the electrorheological fluid on the dynamic behavior of a flexible shaft. The results obtained are discussed and compared with the linear model, which is restricted to only small vibrations around the equilibrium position. A new smart hydrostatic squeeze film damper is proposed to reduce the transient response of the shaft and transmitted forces by applying an electric field to the NER fluid, which results in modifying its viscosity. The results show that it is possible to effectively monitor the electric field and the viscosity of the fluid inside the hydrostatic squeeze film dampers (HSFD) for a better control of flexible shaft vibration and bearing transmitted forces.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.999

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)0.0020.000

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 head0.228
Teacher spread0.222 · 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