The influence of cell micro-structure on the in-plane dynamic crushing of honeycombs with negative Poisson’s ratio
Bibliographic record
Abstract
The in-plane dynamic crushing behaviors and energy-absorbed characteristics of honeycombs with negative Poisson’s ratio (NPR) have been studied by means of explicit dynamic finite element analysis (DFEA) using ANSYS/LS-DYNA. First, the honeycomb models filled with different reentrant cells by the variation of micro-cell configuration parameters (cell-wall angle and shape ratio) are established. The respective influences of micro-structure and impact velocities on the deformation behaviors, the dynamic plateau stresses and the absorbed energy of reentrant honeycombs are explored in detail. It is shown that owing to the variation of cell micro-structure, reentrant honeycombs display different macro-/micro- deformation properties during the crushing. For the given impact velocity, the dynamic plateau stresses are related to the shape ratio by a power law and to the cell-wall angle by least-square curves. And they are also proportional to the square of impact velocities for a high impact velocity. Based on the finite element simulated results and one-dimensional shock wave theory, an empirical formula for auxetic honeycomb to predict the dynamic plateau stress is derived in terms of relative density and impact velocity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".