Developing a Robust Ordering-Based Unstructured Moving Grid Strategy
Bibliographic record
Abstract
In this work, a new method is presented to control the propagation of the boundary deformation within an unstructured mesh domain effectively and to avoid inefficient and unnecessary grid movement computations. In this regard, a new and simple automatic unstructured mesh ordering strategy is developed. The mesh movement algorithm automatically determines the regions, which are affected by the displacement propagation. The method needs little memory storage benefiting from an improved mesh data structure. We also present an improved acceleration strategy, which is highly consistent with the modified connectivity matrix and is able to handle a wide variety of problems with small and large boundary and grid deformations without requiring considerable memory storage. Using successive small deformation strategy and a combination of spring analogy and its torsional aspect, it leads to a robust strategy, which guarantees a qualitative mesh even in large and severe boundary and grid deformations. The current movement algorithm suitably employs the modified connectivity matrix to propagate deformation even to regions far from the moving boundary and provides a higher flexibility to control the displacement directions in problems with a wide variety of deformation magnitudes and directions. The extended method can be equally utilized in different industrial applications such as those in fluid-structure interactions and stochastic shape optimizations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".