Prediction and validation of terramechanics models for estimation of tyre rolling resistance coefficient
Bibliographic record
Abstract
This research focuses on prediction of the rolling resistance coefficient of an agriculture tyre using finite element analysis (FEA) technique, and Bekker and Wismer-Luth models. The tyre-soil interaction is modelled using FEA and smoothed-particle hydrodynamics (SPH) techniques in Visual Environment's PAM-Crash software and validated based on experimental results. A single-wheel tester along with a controlled soil bin at Urmia University, Iran is used to investigate the effect of a tyre's multi-pass and vertical load on the rolling resistance coefficient of an off-road tyre. In order to calculate the rolling resistance of Bekker model, a bevameter device is installed on a carriage moving on clayey-loam soil and a digital penetrometer is used for obtaining the output of Wismer-Luth model. Analysis of experimental data shows that rolling resistance coefficient increases as the vertical load increases and decreases with each pass of tyre. These results are used to compare and evaluate the above-mentioned methods. The results of this study will be used in further research on the interaction between a tyre and soil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".