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Record W2074125237 · doi:10.1115/imece2013-63212

Load Dynamic Analysis of Upper and Lower Arm Automotive Suspension

2013· article· en· W2074125237 on OpenAlexaff
Mohamed Bouazara, Mostepha Saihi, Marc J. Richard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversité LavalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsSuspension (topology)DamperVibrationAutomotive engineeringAutomotive industryStructural engineeringWork (physics)Spring (device)Computer scienceShock absorberVehicle dynamicsMechanical engineeringEngineeringAerospace engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

To reduce weight, in last decade the transport industry had recourse to the use of more lightweight material. Currently, several static elements of the vehicles are made of aluminum. However, the dynamic elements such as suspension parts cause difficulties due to high solicitations from vibration and road roughness. To better assess this damage in depth, the modeling of a full suspension system is more than necessary. In this work, a full quarter vehicle model while considering the motion of suspension along three axes is developed. This system is composed of an upper arm, lower arm, the spring, the damper, the wheel and the fastening elements. By using this full analytical model, all parameters such as, velocities, accelerations and forces can be determined.

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 categoriesInsufficient payload (model declined to judge)
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 score0.423
Threshold uncertainty score0.999

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)0.0020.000

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 head0.234
Teacher spread0.227 · 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.

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
Published2013
Admission routes1
Has abstractyes

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