Modeling and Prediction of Truck Tire Temperature Using Advanced Computational Techniques
Bibliographic record
Abstract
This paper investigates the development of a Finite Element model of a Mixed Service Drive truck tire sized 315/80R22.5 equipped with thermal simulating properties. The physical experiments were performed at a high-speed track in Hällered, Sweden for the truck combination travelling at a constant speed of 80 km/h. For this investigation, the Gross Combination Weight is approximately 42 metric tons. In the Finite Element Analysis environment, ESI Virtual Performance Solutions, the truck tire is designed with hyperelastic Ogden solid rubber definitions. The Ogden material definition is used in this application as it is more suitable to perform thermal and wear analysis within the Finite Element environment. The Finite Element truck tire model is simulated to increase in two different temperature rates. The truck tire model simulates the thermal build-up over time for select tires on a High-Capacity transport truck combination, particularly a driven tire on the tractor. Finite element tire models can be computationally expensive to simulate, especially the build-up of temperature over a long period of time. Experimentally, tires belonging to the tractor increased temperature in a shorter time frame, which is suitable and efficient for developing the FEA simulations.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".