Effects of site operating conditions on real site TKPH (tonne-kilometer-per-hour) of ultra-large off-the-road tires
Bibliographic record
Abstract
The objective of this study is to investigate the effects of site operating conditions on the real site TKPH (tonne-kilometer-per-hour) of ultra-large off-the-road (OTR) tires. To achieve this, a novel finite element OTR tire thermal (OTRTire-T) model was developed to predict the temperatures of OTR tires. As per the results from the OTRTire-T model, the cycle length coefficient K 1 and the site ambient temperature coefficient K 2 were refined and then compared with existing coefficients in the literature for cross-verification. After cross-verification, these K 1 and K 2 coefficients were used to calculate the real site TKPHs. The real site TKPHs were investigated under different site operating conditions (i.e. average vertical tire loads, average cycle speeds, ambient temperatures, and cycle lengths). The results showed that the real site TKPH increased with a rise in average cycle speeds from 10 to 45 km/h and an elevation of ambient temperatures from −30°C to 40°C. At low ambient temperatures below 15°C, as per the real site TKPH, the loading capacity of the truck may increase (compared with its rating payload of 363 t) at mine sites. In addition, the real site TKPH increased relatively rapidly when the cycle lengths were short but rose slowly, or even leveled off, with a further increase in cycle lengths.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".