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Record W4391734634 · doi:10.3329/jsr.v16i1.67441

Modeling and Simulation of Semiactive and Active Suspension System using Quarter Car Model

2024· article· en· W4391734634 on OpenAlexaboutno aff
Vijay Barethiye

Bibliographic record

VenueJournal of Scientific Research · 2024
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Car modelSuspension (topology)Computer scienceControl theory (sociology)Automotive engineeringMathematicsEngineeringArtificial intelligenceHistoryControl (management)

Abstract

fetched live from OpenAlex

A passive suspension system is used by the automotive industry to control the motion of the vehicle. Nowadays, semiactive and active suspension systems are a requirement of commercial vehicles to improve performance characteristics and road comfort. The objective of the paper is to present a comprehensive analysis of passive, semiactive, and active suspension systems. The fuzzy logic controller is used to present the active suspension system. The characteristic of the shock absorber (damper) is non-linear and hysteresis in nature. Hence, a Magneto-rheological (MR) damper-based Bouc-Wen model is utilized to present the semiactive suspension. The comparative analysis of vehicle suspension characteristics has been carried out by using a bump road profile. The modeling of the Bouc-Wen model and quarter car system is carried out in the Simulink environment. The simulated results show that the semiactive and active suspension systems can be a better option for vehicle suspension systems to provide passengers with road comfort.

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)0.002
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: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.082
GPT teacher head0.351
Teacher spread0.270 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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