Crashing analysis and multi-objective optimisation of duplex energy-absorbing structure for subway vehicle
Bibliographic record
Abstract
The emphasis of the study is to investigate the impact performance of the duplex energy-absorbing structure through experiments and numerical simulations. First, a finite element (FE) model of the energy-absorbing structure was established and validated by corresponding experiments. Based on the validated FE model, the effects of the strength of honeycomb on the impact performance of the energy-absorbing structure were evaluated. The results showed that the initial peak force and the average impact force increase with the increasing strength of honeycomb, and the energy absorption also follows the same trend in a certain range of combined strength. Then, to optimise the crashworthiness of the energy-absorbing structure, the multi-objective optimisation, the non-dominated sorting genetic algorithm (NSGA-II) and the polynomial response surface method were adopted, The optima were given in the form of Pareto fronts and the most satisfactory solution was determined by the minimum distance between ‘utopia point’ and knee point. The results showed that whether the best optima can be obtained has nothing to do with the accuracy of the surrogate models. And the structure possesses the best crash performance (EA = 200.8 kJ, Fp = 903.2 kN) when the strengths of honeycombs A and B are 5.23 and 4.00 MPa, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".