Anisotropic 3-D Mesh Adaptation for Turbulent Flows
Bibliographic record
Abstract
Most turbulence models require that the height of the first layer of elements in the boundary layer fall within a specific range of Y + and, in the cases where wall functions are used, that the elements be orthogonal to the wall. In this paper, a 3-D mesh adaptation module is extended to account for these requirements of the turbulence model in order to obtain a mesh more suitable for turbulent solutions on tetrahedral meshes and on hybrid unstructured meshes with layers of prisms on no-slip walls. Y + adaptation for unstructured meshes is implemented by modifying the error metric of near-wall elements to obtain an appropriate grid point distribution in the boundary layer, while providing solutionbased adaptation in the remainder of the domain. For cases where prisms are used, the height of the first prism at the wall is set to a specified Y + , again with solution-based adaptation in the remainder of the domain. For turbulence models with wall functions, a transition flag is developed to detect separation, stagnation, and recirculation zones, and to determine if Y + adaptation is locally appropriate. Results are presented that demonstrate the improvements in the meshes that result from these new functions and the corresponding improvement of the CFD solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".