Parallel implementation of implicit finite element model with cohesive zones and collision response using CUDA
Bibliographic record
Abstract
Summary The aim of this work is to efficiently implement the Park‐Paulino‐Roesler cohesive zone model with the objective of creating realistic high‐resolution simulations of material deformation, fracture, and postfracture behavior. Intrinsically, unstructured meshes can create more realistic fracture patterns in bulk material than structured meshes. Implicit methods, stable for much larger time steps, have greater potential to model both fracture and postfracture behavior without sacrificing speed of execution. Several technical contributions are presented, including (i) GPU‐accelerated implementation of the Park‐Paulino‐Roesler cohesive zone model, (ii) efficient creation of sparse matrix structure, and (iii) comparison of different unloading/reloading relations when using an implicit scheme. A potential‐based collision response scheme was implemented that allows one to model the interaction of fragmented material. Several test simulations are carried out to demonstrate the flexibility of the model and its ability to reproduce different materials under various loading conditions. Benchmarking results show that most of the computational time is spent by the third‐party solver library, meaning that other operations do not require optimization. The library is made available as open source.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".