A Topological Model for the Representation of Meshing Constraints in the Context of Finite Element Analysis
Bibliographic record
Abstract
The preparation of Finite Element analysis models (FE models) from Computer Aided Design (CAD) models is still a difficult task since its Boundary Representation (B-Rep) is often composed of a large number of thin faces, small edges, which are much smaller than the desired element size, and are not relevant for the meshing process. Such inconsistencies often cause poor-shaped FE elements, overdensities of elements, not only slowing down the computation of the FE solution, but also producing poor simulation results. In this paper, we present a “Mesh Constraint Topology” (MCT) model with adaptation operators aiming at transforming the CAD model in a FE model which only contains meshing-relevant edges and vertices, i.e. the explicit model of data intrinsic to the meshing process. Because the topology of faces adapted for meshing could contain interior edges, the MCT is represented with adjacency graphs instead of the B-Rep data-structure. We demonstrate how graphs provide efficient schemes to qualify interior and boundary entities, and facilitate the design of adaptation operators using high-level graph operators. Application and results are presented through adaptation issues of CAD models solved using MCT adaptation operators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".