Numerical Investigation of Temperatures in Ultra-Large Off-the-Road Tires Under Operating Conditions at Mine Sites
Bibliographic record
Abstract
Abstract The objective of this study is to conduct a numerical investigation to examine the temperatures in off-the-road (OTR) tires under operating conditions at mine sites. To achieve this, a new mathematical equation was developed based on a modified Mooney–Rivlin (MR) strain energy function, the pseudo-elasticity theory, and the inverse analysis method. This equation was used to determine the internal heat generation rates of tire rubbers. With heat generation rates, the governing equation of heat conduction and the mathematical expression of boundary conditions were further generated to describe the heat transfer in tire rubbers. Based on these equations, a novel finite element (FE) OTR tire thermal (OTRTire-T) model was developed. This OTRTire-T model was used to numerically investigate temperatures in OTR tires at vertical loads from 0.34 to 1.04 MN, hauling speeds from 5 to 30 km/h, and ambient temperatures from −30 to 40 °C. The results showed that a large vertical load (e.g., 1.04 MN) increased the tire rubber temperatures considerably. Tire rubber temperature also increased with an increase in hauling speeds, and the increase became more significant at larger vertical loads (e.g., 1.04 MN). The OTRTire-T model identified an inverse proportional relationship between the rubber temperature increments and the ambient temperatures from −30 to 40 °C. Nonetheless, the rubber temperature in the OTR tire increased relatively rapidly with an increase in ambient temperatures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".