Hybrid Lattice Particle Modelling Approach for Polymeric Materials Subject to High Strain Rate Loads
Bibliographic record
Abstract
Hybrid Lattice Particle modelling (HLPM) is an innovative particular dynamics approach that is established based on a combination of the particle modelling (PM) technique together with the conventional lattice modelling (LM) theory. It is developed for the purpose of simulating the dynamic fragmentation of solids under high strain rate loadings at macroscales with a varying Poisson's ratio. HLPM is conceptually illustrated by fully dynamic particles (or “quasi-particles”) placed at the nodes of a lattice network without explicitly considering their geometric size. The interaction potentials among the particles can employ either linear (quadratic) or nonlinear (Leonard-Jones or strain rate dependent polynomial) type as the axial/angular linkage. The defined spring constants are then mapped into lattice system, which are in turn matched with the material’s continuum-level elastic moduli, strength, Poisson's ratio and mass density. As an accurate dynamic fracture solver of materials, HLPM has its unique advantages over the other numerical techniques which are mainly characterized as easy preparation of inputs, high computation efficiency, ability of post-fracture simulation and a multiscale model, etc., This paper is to review the successful HLPM studies of dynamic fragmentation of polymeric materials with good accuracy. Polymeric materials, including nylon 6-6, vinyl ester and epoxy, are accounted for under the loading conditions of tension, indentation and punctuation. In addition, HLPM of wave propagation and wave induced fracture study is also reviewed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".