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Record W2766173623 · doi:10.1115/detc2017-67343

A Novel Performance Analysis Method for a Full Vehicle Suspension Based on Quarter Car Model

2017· article· en· W2766173623 on OpenAlexaboutno aff
Long Wu, Lei Zuo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSprung massCar modelSuspension (topology)Vehicle dynamicsQuarter (Canadian coin)Automotive engineeringVibrationCoupling (piping)EngineeringComputer scienceMechanical engineeringMathematicsAcousticsPhysics

Abstract

fetched live from OpenAlex

In vehicle dynamics researchers traditionally investigate the suspension performance based on a quarter car model and then reestablish a comprehensive model for the full car by considering additional degrees of freedom (DOF). Based on the derivation of the coupling ratios between the sprung mass of a full car and four sprung masses of quarter cars, the analysis of a full vehicle dynamics with fourteen DOFs in vertical and lateral directions is possible. The full car model can be expressed by four independent quarter car models. An analysis method will be investigated in order to provide a novel performance estimation for a full vehicle suspension. The case study shows that the vibrations of a full vehicle can be quantitatively obtained based on the test results of quarter suspensions.

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 score0.605
Threshold uncertainty score0.491

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 head0.022
GPT teacher head0.267
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

Citations0
Published2017
Admission routes1
Has abstractyes

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