Finite element analysis and multi-stage cooperative optimization of the expansion-tearing energy absorption structure
Bibliographic record
Abstract
The expansion-tearing tube structure not only achieves continuous and stable energy absorption over a long stroke but also rapidly dissipates energy under high-impact loads, ensuring optimal energy absorption performance. Due to its unique structural characteristics and superior mechanical properties, this design provides valuable insights for developing energy-absorbing structures in vehicles, trains, and aircraft. In this study, a multi-stage cooperative optimization algorithm, integrating multi-objective optimization and multi-criteria decision-making theories, is proposed to address the selection and optimization of the expansion-tearing energy absorption structure. Finite element modeling is conducted, and the model’s validity is verified through experimental data. Subsequently, a crashworthiness sensitivity analysis of the structure’s parameters is performed. Based on the proposed optimization algorithm, the structural parameters are further refined, and the optimal crashworthiness configuration is identified. The results demonstrate that the optimized design obtained through this algorithm is highly reliable, with significant improvements in the overall crash performance of the expansion-tearing energy absorption structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".