A novel phenomenological model for predicting hysteresis loss of rubber compounds obtained from ultra-large off-the-road tires
Bibliographic record
Abstract
The objective of this study was to develop a novel phenomenological model that can predict the hysteresis loss of rubber compounds obtained from ultra-large off-the-road (OTR) tires under typical operating conditions at mine sites. To achieve this, first, cyclic tensile tests were conducted on tire tread compounds to derive the experimental results of hysteresis curves, peak stress, residual strain, and hysteresis loss at 6 strain levels, 8 strain rates, and 14 rubber temperatures. Then, referring to these experimental results, a phenomenological model was developed – the HLSRT model (a hysteresis loss model considering strain levels, strain rates, and rubber temperatures). This HLSRT model was generated based on a novel strain energy function that was modified from the traditional Mooney-Rivlin (MR) function, and the model was used to predict the hysteresis loss of rubber compounds in OTR tires. The prediction results show that the HLSRT model estimated the hysteresis loss of tire tread compounds with average and maximum mean absolute percent errors (MAPEs) of 11.2% and 18.6%, respectively, at strain levels ranging from 10% to 100%, strain rates from 10% to 500% s −1 , and rubber temperatures from −30°C to 100°C. These MAPEs were relatively low when compared with previous studies, showing that the HLSRT model has higher prediction accuracy. For the first time, the HLSRT model derived from this study has provided a new approach to predicting the hysteresis loss of OTR tire rubbers to guide the use of OTR tires in truck haulage at mine sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".