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Record W2143256264 · doi:10.1115/imece2010-40402

Study of Dynamic Performance of a Vehicle With Planar Suspension Systems in a Split-µ Braking-in-Turning

2010· article· en· W2143256264 on OpenAlexaff
Jian Jun Zhu, Amir Khajepour, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsOntario Tech UniversityUniversity of Waterloo
Fundersnot available
KeywordsChassisSuspension (topology)Automotive engineeringVibrationVehicle dynamicsTransient (computer programming)Shock absorberCommercial vehicleTire balanceEngineeringControl theory (sociology)Computer scienceStructural engineeringAcousticsPhysicsControl (management)

Abstract

fetched live from OpenAlex

A planar suspension system (PSS) has spring-damping struts in both the vertical and longitudinal directions so that the vibrations and shocks caused by road obstacles in any direction within the wheel plane can be effectively absorbed. Consequently, the ride comfort of a PSS vehicle can be improved considerably compared to a conventional vehicle. For a vehicle with such suspension systems, however, the wheels can move forth and back with respect to the chassis. The dynamic behaviors of a PSS vehicle under some special conditions, such as a split-μ turning combined with braking operation, may exhibit different characteristics. This paper presents the study of the transient response of a vehicle with PSS in such a case. The simulation results are also compared with those of a similar vehicle with conventional suspension under the same operational condition. The study demonstrates that the handling behavior of a PSS vehicle is generally comparable with, and in some conditions, even better than that of a conventional vehicle.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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