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Record W2119148051 · doi:10.1109/icnsc.2006.1673198

Relative Assessments of Current Dependent Models for Magneto-Rheological Fluid Dampers

2006· article· en· W2119148051 on OpenAlexaff
Xiao Qing, Subhash Rakheja, Chun‐Yi Su

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsSigmoid functionCurrent (fluid)Nonlinear systemDamperFunction (biology)HysteresisExcitationControl theory (sociology)PolynomialRheologyPhysicsComputer scienceMathematicsEngineeringMathematical analysisStructural engineeringArtificial neural networkThermodynamicsArtificial intelligence

Abstract

fetched live from OpenAlex

The magneto-rheological dampers exhibit hysteretic and nonlinear force-velocity characteristics, which are strongly dependent upon the nature of excitation and applied current. An independent current function is proposed that could enhance the current-dependent damping force prediction ability of the selected models, when integrated with the hysteretic force function. These models included modified linear biviscous, polynomial, extended Bouc-Wen and generalized sigmoid function models. The validity of the modified models and the proposed current function is examined by comparing the model results with the measured data under different currents and excitation conditions. The results show that the integration of the proposed current function could significantly enhance the performance of all the models in predicting current-dependent hysteretic damping force. The relative error analyses revealed that the modified Bouc-Wen and sigmoid function models can provide reasonably good characterization of the nonlinear and hysteretic MR damping force over range of currents and excitation conditions considered in the study. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

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: none
Teacher disagreement score0.972
Threshold uncertainty score0.368

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 head0.029
GPT teacher head0.273
Teacher spread0.245 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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