Prediction of All-Steel CNG Cylinders Fracture in Impact by Using Damage Mechanics Approach
Bibliographic record
Abstract
In this paper damage mechanics approach is used to investigate the effect of crash and damage caused by impact in steel cylinder filled by Compressed Natural Gas (CNG). The Canadian Standard Association (CSA) for CNG cylinders is used as a damage detection criterion and cylinders ability to reuse. Johnson-Cook damage model is used to compute the cylinder failures. Simulations are carried out in different impact directions, and the effect of cylinder internal pressure, collision velocity and the fall height are analyzed. Also failures due to collision for various situations are studied and discussed. Investigations for cases including crash and drop tests showed that the maximum damage in cylinder is created for the case of normal impact and by changing the impact direction from normal to side, the amount of damage will be decreased. Also by eliminating failed elements and comparing the damage depth caused by collision using the CSA standard, it was observed that in the most cases of normal accident and drop tests, cylinders have been damaged and lose its ability to use. However, in side impact cases cylinder is intact or can be reused after repairing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".