Simulation-Based Methodology for Determining the Dynamic Strength of Tire Inflation Restraining Devices
Bibliographic record
Abstract
The article suggests and supports a simulation-based methodology for determining whether the dynamic strength of tire inflation restraining devices for tire inflation meet quality requirements and ensure operator safety during a potential tire explosion. Dynamic strength tests using an NM-600 safety shield and NK-0728 safety cage during a 29.5 R25X tire explosion at a pressure of 10 bar were presented as an example application of this methodology. The shield was subjected to destructive tests involving the use of a 2200 kg impactor, dropping it so that the minimum kinetic energy reached 20 kJ at the time of impact. Analyzed devices were constructed of S355 steel in accordance with EN 10025. The Cowper–Symonds model of material for strain rate phenomena was used in the calculations. Simulations of a 20 kJ ring impact against the cage were performed. The equivalent stress distribution was determined, and displacement contour lines for the maximum dynamic deformation value and plastic deformation were calculated. The plastic displacement obtained in numerical tests was equal to the permanent deformation recorded in the experimental test. Further, the simulations showed that the examined cage met the assumed strength criteria. The conducted tests confirmed the usefulness of the proposed methodology for assessing the dynamic strength of safety cages and shields for tire inflation. The full-scale, physical cage testing is difficult to implement because it requires placing a ring impacting the cage wall. This is a major boundary for closed cages, as considered in this publication. Thus, simulation-based methods are becoming a principal tool for safety assessment of tire inflation restraining devices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".