Implementation, verification and validation of the multi-surface failure envelope for ice in explicit FEA (LS-DYNA) with full derivation of it's invariant form
Bibliographic record
Abstract
The multi-surface failure criterion/model for ice is implemented into an explicit finite element program called LS-DYNA (www.lstc.com). The implementation of the model equations was achieved via the development of a user routine for LS-DYNA. Since ice behavior depends on temperature, strain/load rate and loading direction (loading direction for the case of anisotropic ice), the traditional isotropic and temperature and rate independent failure criterion such as von-Mises and Mohr-Coulomb are not applicable. The multi-surface failure criterion (Derradji-Aouat, 2003) accounts for those effects and is formulated with the parameters of the octahedral shear stress and hydrostatic pressure. In this paper, a 3-step procedure will be presented and discussed to demonstrate successful multi-surface failure model implementation in explicit FEA. Step 1 is concerned with the theoretical implementation of the ice multi-surface failure model into the explicit commercial code LS-DYNA. Step 2 deals with verification and validation of the FE implementation, this step may be called V&V analysis and its rooted in uncertainty methodologies and statistical analyses. V&V will be based on comparisons between the numerical results and the results from the ice compression tests done by Rist and Murrell (1994) which was one of the data sources for the present failure criterion. Step 3 deals with showing how the implemented model in LS-DYNA predicts actual ice pressure and indentation tests on an ice block using the MTS machine in the cold room (Wells et al., 2008) and are simulated as an example. Discussion regarding the multi-surface model, its implementation, numerical results, and model predictions are presented.
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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) | 0.000 | 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) | 0.005 | 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".