Energy-absorbing structures based on staggered composite cutting rings: Crashworthiness and railway applications
Bibliographic record
Abstract
The aim of this study was to develop a high-performance energy-absorbing device with both high energy absorption and smooth energy dissipation. Drawing upon the concept of exploiting temporal misalignment of impact force, a novel composite energy-absorbing device with staggered combination of cutting rings (CECR) was investigated. Dynamic impact tests were conducted using a drop hammer system, and a finite element model of CECR was built to study its application in railway vehicles. The study showed that the cutting rings fail into filamentous fine circles under impact loads, exhibiting high metal utilization efficiency. Under the influence of staggered combination of cutting rings, CECR demonstrated impact force misalignment compensation, with smooth impact forces. The average impact force reached 351.59 kN, with a maximum energy absorption of 195.37 kJ. The FE simulation model of CECR provided good simulation of failure modes, impact force, and energy absorption. Application of CECR to railway vehicles, with a collision simulation of the entire vehicle at 36 km/h, showed a 91.14% increase in steady-state impact force and a significant improvement in passive safety protection capability. CECR can provide design concepts and guidance for the development of energy-absorbing devices with smooth energy absorption characteristics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".