Effects of regenerative mechanical vibration on the mechanical integrity of ceramic diesel particulate filters
Bibliographic record
Abstract
In this study, the effects of mechanical vibration on the mechanical properties of ceramic diesel particulate filters (DPFs) were investigated. The goal is to determine how the mechanical vibration used in the regenerative ash cleaning process for these filters affects their mechanical integrity during subsequent reuse. Both virgin and vibrated DPF samples were subjected to com-pressive and 3-point flexural loading at three different loading rates along axial and tangential directions. Statistical analysis was conducted to determine the significance of variation in the compressive and flexural strengths of the DPFs as a result of exposure to mechanical vibration. The results show that there is no statistically significant difference in both compressive and flexural strengths of the virgin DPFs and the DPFs subjected to the same level of mechanical vibration typically used in ash cleaning of DPFs. When the intensity of vibration was doubled, the drop in compressive strength became statistically significant, but less than 10% under axial loading. However, no drop in flexural strength was observed for DPFs subjected to this high intensity of mechanical vibration. The safe threshold for mechanical vibration of ceramic filters is considered to be much higher than that currently used in vibration-based ash cleaning process.
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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) | 0.000 | 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) | 0.001 | 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".