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Record W3139646554 · doi:10.1109/iros.2011.6048200

Wheel-soil interaction model for rover simulation based on plasticity theory

2011· article· en· W3139646554 on OpenAlexaff
Ali Azimi, József Kövecses, Jorge Angeles

Bibliographic record

Venue2011 IEEE/RSJ International Conference on Intelligent Robots and Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceAerospace engineeringSimulationEngineering

Abstract

fetched live from OpenAlex

A novel approach is proposed for the modeling of rigid-wheel and soft-soil interaction using infinitesimal plasticity theory. Our motivation is the need to have a model compatible with dynamic models of multibody systems. The idea is to use a prescribed velocity field in the vicinity of the contact area based on the physics of the problem, and find the changes in the stress field by resorting to plasticity theory and an appropriate constitutive relation for soil. In this paper the Drucker-Prager constitutive relation with cap hardening is employed. The proposed approach leads to results that tally with those obtained using well-established semi-empirical models. The calibration of the semi-empirical model parameters is done by performing simulation runs using detailed FEM models with the Abaqus/Explicit software package.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.114
GPT teacher head0.290
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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