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Record W2948747678 · doi:10.11159/iccste19.191

Multileaf Spring Model and Its Behaviour in a Tandem Bogie Layout

2019· article· en· W2948747678 on OpenAlexvenueno aff
Martin Maloch, Štefan Čorňák

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
FundersMinisterstvo Obrany České Republiky
KeywordsBogieSpring (device)Computer scienceTandemEngineeringStructural engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The utilization of independent suspension is rising. For a very specific conditions, the dependent suspension types are still preferred, mainly in case of heavier trucks. Ordinary leaf spring material-steel is being replaced by a variety of composite materials. For the dynamic simulation of the vehicle, the greater importance than a material type, lies in the layout of the suspension. The tandem bogie layout of the multileaf spring suspension has many differences in its behaviour in comparison with ordinary layout-set of two separated multileaf springs allocated to one axle. In the first part of the paper, these differences are listed and explained-mainly the ability of load sharing between the axles. The second part deals with specific aspects of the FEM model, along with the necessary analytical background. The third part consists of various simulation cases with different initial conditions-different constrains, pretension and spring as tyre stiffness. In the last part, the effect of the aforementioned conditions are evaluated and commented. The variations of the hysteresis loops, in force-displacements characteristics, along with derived linear stiffness's and its behaviour are recommended to be understood for additional application. Therefore, the conclusions are drawn for further utilization of gathered data for the full vehicle simulation.

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: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.405

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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