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Record W4408632582 · doi:10.37934/araset.63.3.186200

Analysis of Quarter-Car Suspension Dynamics in Sports Cars with PID Control

2025· article· en· W4408632582 on OpenAlexaboutno aff
Arman Haditiansyah, Faathir Alfath Risdarmawan, Bella Nabila, Putri Wulandari, Octarina Nur Samijayani, Ary Syahriar

Bibliographic record

VenueJournal of Advanced Research in Applied Sciences and Engineering Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PID controllerSuspension (topology)Dynamics (music)Car modelControl (management)Automotive engineeringAeronauticsEngineeringComputer scienceMathematicsGeographyPhysicsControl engineeringArtificial intelligenceTemperature controlAcousticsArchaeology

Abstract

fetched live from OpenAlex

The analysis and modeling of a sports cars suspension using a PID controller aims to minimize vibrations, effectively shifting the system from underdamped to nearly critically damped, thus enhancing comfort. This research emphasizes the significance of the mass-spring-damper model in suspension analysis, particularly highlighting the role of PID control in reducing vibrations for improved user comfort in sports cars. The comprehension of mechanical system dynamics is facilitated by utilizing the mass-spring-damper system as a mathematical tool, drawing parallels between suspensions and such systems, where energy is stored, similar to the shock absorption in a suspension. Ride comfort, a pivotal aspect of vehicle performance, often faces disruption due to road-induced vibrations, which suspensions manage to mitigate discomfort and disorder. Through quarter-car models, this research delves deep into suspension dynamics, contributing to optimizing suspensions for elevated ride comfort, stability, and enhanced handling.

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)0.001
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 score0.046
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
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 head0.005
GPT teacher head0.253
Teacher spread0.248 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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