A node‐pair finite element/volume mesh adaptation technique for compressible flows based on a hierarchical approach
Bibliographic record
Abstract
SUMMARY A grid adaptation technique for two‐dimensional unstructured grids of triangles and quadrilaterals is presented. The error estimation procedure is formulated in terms of a node pair‐based data structure that allows for a unified description of the finite element and finite volume schemes. The adaptation algorithm is based on a strategy of hierarchical corrections, where a suitable number of intermediate adapted grids are generated and successively corrected by employing a simple node insertion technique at the midpoint of the element edges. Coarsening of the grid is obtained in an implicit fashion by avoiding the insertion of new nodes during the correction phase. The adaptation history, from the initial to the current grid, including all intermediate grids, is stored and updated through the whole process. No intermediate grid is therefore required to be stored explicitly. The adapted grid is anisotropic, thanks to the adoption of both regular triangular and quadrilateral elements with high aspect ratio that are gathered in, for example, boundary layer or wake regions. Numerical experiments of steady compressible flows, including both inviscid and viscous flows, are presented to support the suitability of the adaptation technique. Copyright © 2012 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".