Effect of Hierarchical Geometries Matching on the Crashworthiness of Honeycomb
Bibliographic record
Abstract
Structural hierarchy has become a popular technique to improve the crashworthiness of engineering structures. A study is conducted to explore the interaction between hierarchical geometries and determine the optimum honeycomb configuration that improves crashworthiness. Nine distinct second‐order vertex‐based hierarchical honeycombs are constructed by iteratively replacing the vertices of a square‐based honeycomb with squares, circles, and octagons. Validated finite element models are then established to investigate the out‐of‐plane crashworthiness performance. Subsequently, the effect of the hierarchical geometry combinations and cell length ratios on the crashworthiness performance of the nine honeycombs is studied. The study showed that the second‐order hierarchical honeycomb exhibited superior crashworthiness performance under the same relative density compared to the regular and first‐order hierarchical square honeycombs. The study determined that the circle is a suitable matching geometry in the first‐ and second‐order hierarchies for improving the crashworthiness of a square‐based honeycomb. Using Complex Proportional Assessment, the Square‐Circle‐Circle ranked as the optimum structure for crashworthiness application.
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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".