A New Continuum-Based Thick Shell Finite Element for Soft Biological\n Tissues in Dynamics: Part 1 - Preliminary Benchmarking Using Classic\n Verification Experiments
Bibliographic record
Abstract
For the finite element simulation of thin soft biological tissues in\ndynamics, shell elements, compared to volume elements, can capture the whole\ntissue thickness at once, and feature larger critical time steps. However, the\ncapabilities of existing shell elements to account for irregular geometries,\nand hyperelastic, anisotropic 3D deformations characteristic of soft tissues\nare still limited. As improvement, we developed a new general nonlinear thick\ncontinuum-based (CB) shell finite element (FE) based on the Mindlin-Reissner\nshell theory, with large bending, large distortion and large strain\ncapabilities, embedded in the updated Lagrangian formulation and explicit time\nintegration. We performed numerical benchmark experiments available from the\nliterature that focus on engineering linear elastic materials, which, verified\nand proved the new thick CB shell FE to: 1) be accurate an efficient 2) be\npowerful in handling large 3D deformations, curved geometries, 3) accommodate\ncoarse distorted meshes, and 4) achieve comparatively fast computational times.\nThe new element was also insensitive to three types of locking (shear, membrane\nand volumetric), and warping effects. The capabilities of the present thick CB\nshell FE in the biomedical realm are illustrated in a companion article (Part\n2), in which anisotropic incompressible hyperelastic constitutive relations are\nimplemented and verified.\n
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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) | 0.004 | 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".