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Record W2898804095 · doi:10.1115/detc2018-85006

Development of In-Plane Truck Tire-Flooded Surface Interaction Models Using FEA-SPH Techniques

2018· article· en· W2898804095 on OpenAlexaff
Zeinab El-Sayegh, Moustafa El–Gindy, Inge Johansson, Fredrik Öijer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTreadContact patchTruckTerrainFinite element methodInclined planePlane (geometry)Tire balanceRadial tireEnhanced Data Rates for GSM EvolutionVertical planeNode (physics)Structural engineeringMaterials scienceComputer scienceEngineeringAutomotive engineeringMechanical engineeringNatural rubberGeometryComposite materialMathematics

Abstract

fetched live from OpenAlex

The performance of a vehicle highly depends on the tire-terrain interaction characteristics. The terrain on which a vehicle operates can vary dramatically. This paper focuses on the evaluation of an in-plane truck tire performance running over the flooded surface. The truck tire is modeled using Finite Element Analysis (FEA) technique and validated against measured data. The water is modeled using Smoothed Particle Hydrodynamics (SPH), which includes water material properties. The tire-terrain interaction algorithm is defined using node-symmetric node-to-segment contact with edge treatment. The performance characteristics of the interaction include the rolling resistance coefficient, vertical, longitudinal tread and longitudinal tire stiffnesses. The simulations are repeated for several operating conditions such as inflation pressure, applied vertical load, and water depth. The flooded surface results are compared with previously published data. This work will be extended to include the prediction of the full in-plane and out-of-plane rigid ring tire model parameters while the tire is operating under various conditions.

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 score0.205
Threshold uncertainty score0.397

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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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