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Record W4315697846 · doi:10.1504/ijvsmt.2022.128184

Tyre-terrain interaction modelling and analysis: literature survey

2022· article· en· W4315697846 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Vehicle Systems Modelling and Testing · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsConcordia UniversityOntario Tech University
Fundersnot available
KeywordsTerrainFinite element methodSmoothed-particle hydrodynamicsEngineeringSnowWork (physics)CalibrationVehicle dynamicsGeotechnical engineeringMarine engineeringComputer scienceAerospace engineeringMechanical engineeringStructural engineeringMeteorologyGeographyMechanicsMathematicsPhysics

Abstract

fetched live from OpenAlex

Depending on whether the vehicle is used off-road or on-road, the terrain on which it operates can range from hard surfaces to deformable surfaces, such as soil and snow. It is well known that the soft terrain characteristics have a significant impact on off-road vehicle performance, therefore modelling and analysing the soils and tyres are critical. This paper reviews the available published work related to tyre-terrain interaction modelling and testing. The tyre mechanics fundamentals, as well as the modelling and validation methods used for developing the finite element analysis (FEA) tyres, are discussed in detail. The techniques used for soil modelling and calibration such as FEA, and smoothed particle hydrodynamics (SPH) are also discussed. In addition, the tyre-terrain contact algorithm is addressed.

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.

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)0.001
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 score0.221
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.250
Teacher spread0.213 · 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