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Record W2207245934 · doi:10.1115/imece2014-38960

Hunting Characteristics of a Freight Car in Presence of Secondary Suspension Non-Smooth Contact Dynamics

2014· article· en· W2207245934 on OpenAlexaff
Iman Hazrati Ashtiani, A.K.W. Ahmed, Subhash Rakheja, Jimin Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsTruckSuspension (topology)AxleBifurcationNonlinear systemVehicle dynamicsContact patchStructural engineeringHopf bifurcationContact forceWedge (geometry)EngineeringContact dynamicsControl theory (sociology)MechanicsComputer scienceAutomotive engineeringMaterials sciencePhysicsClassical mechanicsTreadMathematicsGeometry

Abstract

fetched live from OpenAlex

In this study, the nonlinear damping characteristics of friction wedges in the secondary suspension of a freight truck are investigated considering non-smooth contact, geometry, loss of contact and multi-axis motions. The friction wedge model is integrated to a nonlinear multi-body dynamic model of a three-piece truck to study its hunting characteristics. The 114-degrees-of-freedom model also integrates constraints due to side bearings, axle boxes and center plates, while the wheel/rail contact forces are obtained using FASTSIM algorithm considering non-elliptical contact. The parameters of contact pairs within the suspension are identified to achieve smooth and efficient numerical solutions, while ensuring adequate accuracy. The simulation results are presented to illustrate the hunting properties of the truck in terms of critical speed and oscillation frequency. The results showed subcritical Hopf-bifurcation in the lateral dynamic responses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.417

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 head0.003
GPT teacher head0.177
Teacher spread0.174 · 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
Published2014
Admission routes1
Has abstractyes

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