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Record W2789215074 · doi:10.4172/2168-9873.1000299

Vibration Control in Quarter-Car Model with Magnetorheological (MR) Dampers Using Bingham Model

2018· article· en· W2789215074 on OpenAlexaboutno aff
Sakshi Sharma

Bibliographic record

VenueJournal of Applied Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsDamperMagnetorheological fluidVibrationQuarter (Canadian coin)Magnetorheological damperVibration controlComputer scienceControl (management)EngineeringStructural engineeringAutomotive engineeringArtificial intelligencePhysicsAcoustics

Abstract

fetched live from OpenAlex

A semi-active suspension system using Magnetorheological (MR) damper overcomes all the inherent limits of passive and active suspension systems and combines the advantages of both. This paper gives a concise introduction about the suspension system of passenger vehicle which is presented along with the analysis of semiactive suspension system using MR dampers based on Bingham model. Magneto rheological (MR) dampers are filled with magnetorheological fluids whose properties can be controlled by applying voltage signal. To further prove the statement, a quarter car models with two degrees of freedom has been used for modelling the suspension system, the sprung mass acceleration of passive suspension system has been compared with the semi-active suspension system using Bingham model for MR damper. Simulink/MATLAB is used to carry out the simulation. The results drawn show that the semi-active suspension system performed better than the passive suspension system in terms of vehicle stability.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.777

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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
Published2018
Admission routes1
Has abstractyes

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