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Record W2139295130 · doi:10.1109/cdc.2004.1429517

Modeling asymmetric hysteretic properties of an MR fluids damper

2004· article· en· W2139295130 on OpenAlexaff
En Rong Wang, Qing Xiao, Subhash Rakheja, Chiu-Hun Su

Bibliographic record

Venue2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) · 2004
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsDamperHysteresisControl theory (sociology)Displacement (psychology)Nonlinear systemSigmoid functionExcitationParticle displacementAmplitudeMagnetorheological fluidMechanicsCurrent (fluid)PhysicsStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

A generalized model is synthesized to characterize the asymmetric hysteretic force-velocity properties of a magneto-rheological (MR) fluid damper as a function of the command current, excitation frequency and displacement amplitude, on the basis of symmetric and asymmetric sigmoid functions. The synthesis incorporates the peak force, peak velocity, transition velocity leading to force-limiting and the corresponding force, low and high velocity rise, and the hysteresis, in compression as well as rebound under different levels of command current. The model parameters are identified using the measured and modified data for a MR-damper. The validity of the model is examined by comparing the model results with the measured data for both dampers over a broad range of excitation conditions, and applied current in case of the MR damper. It is concluded that the proposed model could be effectively applied to characterize the hysteretic nonlinear properties of a controllable MR damper for development of an optimal controller in vehicle suspension system.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.230
Teacher spread0.202 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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Same venue2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)Same topicVibration Control and Rheological FluidsFrench-language works237,207