Investigation of simulation methodologies for ultra-high–molecular-weight polyethylene
Bibliographic record
Abstract
This work deals with numerical simulations of impact problems on fiber-based composite armor using the commercial finite-element-code ANSYS AUTODYN.Having presented some basic knowledge on the theory of numerical simulation in AUTODYN, two recently published approaches for modeling impact on the selected composite (Dyneema® HB26) are explained.Although both of them make use of a nonlinear-orthotropic material model implemented in the AUTODYN-code, they differ in the way how the highly inhomogeneous microstructure of HB26 is represented geometrically.Lässig chooses a fully homogeneous description, whereas Nguyen discretizes the composite into sublaminates, which are kinematically joined at the surfaces and breakable when a certain contact-stress is reached.In order to validate the two approaches, the response of HB26-samples impacted by handgun-projectiles was determined experimentally and compared to the corresponding numerical results.Unfortunately, a poor agreement between experimental and numerical results was found, which gave rise to the development of an alternative modeling approach.In doing so, the composite was subdivided into alternating layers of two different types.While the first type of layers was modeled with open-literature properties of UHMWPE-fibers, polymer-matrix-behavior was assigned to the second type.Having adjusted some of the parameters, good agreement between experiment and simulation was found with respect to residual velocity and depth of penetration for the considered impact situations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".