Robust numerical approaches for simulating the buckling response of 3D fiber-metal laminates under axial impact – Validation with experimental results
Bibliographic record
Abstract
The reliability and efficiency of three different numerical modeling approaches for simulating the response of a newly developed 3D fiber-metal laminate (3D-FML), subject to axial impact loading, are considered in this paper. The main objective of the study is to establish the most robust numerical framework for analyzing the performance of such complexly configured hybrid materials subject to axial impact loading in a fairly accurate, yet efficient manner. LS-DYNA finite element software is used for the purpose. The models include: (i) a full 3D solid model, where all 3D-FML constituents are modeled with 3D elements; (ii) a model with intermediate complexity, in which two different element types are used to model the metallic skins and 3D-fiberglass/foam core, respectively; and (iii) a simplified scheme, consisting of a single layer of thin-shell elements, representing all constituents of the FML. An experimental investigation is also conducted in parallel to verify the accuracy of the modeling schemes. Force and axial-shortening histories, energy absorption capacity, and overall qualitative behavior obtained numerically are compared to experimental results. Both accuracy and computation cost are considered as the performance criteria, all with the aim of providing the reader with some perspective for robust modeling of such geometrically sophisticated composites, subject to a complex loading mechanism.
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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 | 0.002 |
| 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) | 0.002 | 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".