The multi-objective structural optimisation design to improve the crashworthiness of a multi-cell structure for high-speed train
Bibliographic record
Abstract
This article addresses the energy absorption and crashworthiness optimisation of a multi-cell energy-absorbing structure which is composed of orthohexagonal tubes and connecting column under dynamic impact loading. In this work, a finite element model was established and effectively verified using experimental data. To explore the effects of each structural part length parameters on crashworthiness characteristics such as the specific energy absorption (SEA) and the impacting force efficiency (IFE). The influence of design variables on the impact response is evaluated based on the parametric study of surrogate model. The results show that side lengths at different locations have different effects on SEA and IFE. The characteristics and trends of different variables are different. In addition, based on the surrogate models, to maximise the SEA and IFE under the constraint of design space, multi-objective optimisation design was carried out using multi-objective genetic algorithm. The optimised structure has significant improvement in SEA, IFE and space.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".