Contact Simulation in Finite Deformation — Algorithm and Modelling Issues
Bibliographic record
Abstract
Many problems, involving finite deformation, necessitate an accurate treatment of sliding interface boundaries and impact conditions. Examples of this are the accidental impact of radioactive waste transportation packages, crashworthiness, fluid-structure interaction and various manufacturing processes including metal forming and cutting. In terms of general use and applicability, large scale nonlinear simulations involving arbitrary contact continues to pose a number of challenging issues. Faster computing processors and parallel processing strategies have helped to mitigate the impact of some of these issues on modelling. However, there is room for overall improvement in accuracy, efficiency and user-friendliness of contact algorithms used in the modelling of multiple body impact/contact, eroding surfaces where new free contact surfaces are created, large interface motions of initially unconnected surfaces and post-buckling behaviour of structures where surfaces fold onto themselves. For example, the benefits of using a faster processor with an iterative solver on an elliptic class of problem could be quickly neutralized if the number of iterations rise due to inaccuracies in the contact algorithm. In this paper, salient features of state-of-the-art general three-dimensional contact algorithms for use in finite element software based on explicit and/or iterative solution techniques are reviewed and some of the more complex modelling issues faced by users of such algorithms are addressed.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".