Simulation-Based Methodology for Determining the Dynamic Strength of Tire Inflation Restraining Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it