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Record W4389463249 · doi:10.4271/02-17-01-0002

Multibody Dynamics Modeling of a Continuous Rubber Track System: Part 1—Model Description

2023· article· en· W4389463249 on OpenAlexaff
Antoine Faivre, Olivier Duhamel, Masoud Hemmatian, Jean‐Sébastien Plante

Bibliographic record

VenueSAE International journal of commercial vehicles · 2023
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMultibody systemBushingDiscretizationComputer scienceEngineering design processTrack (disk drive)Mechanical engineeringControl engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

<div>Continuous rubber track systems for farming applications are typically designed using multiple iterations on full-scale physical prototypes which is costly and time consuming. The development of numerical design tools could speed up the design process and reduce development costs while improving product performance. In this article, a rigid multibody dynamics (MBD) model of a continuous rubber track system is presented. This article is the first part of a two-part study: Part 1 focuses on the model description and part 2 describes the experimental evaluation of the MBD model. The modeling methodology is based on a track discretization as a set of rigid body elements interconnected by 6 degrees-of-freedom bushing joints. The mathematical formalism and experimental characterization of all critical subsystems such as the roller wheels, tensioner, suspensions, and contact models are also presented. Several simulation results are presented to illustrate the capability of MBD models in design activities to rapidly evaluate new concepts as a result of the relative short-solving time of the model (approximately 3 h). The MBD model is shown to be a powerful design tool to provide input boundary conditions for structural analysis or as a design space explorer and optimization tool.</div>

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 score0.086
Threshold uncertainty score0.632

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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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