AN ADAPTIVE MESH REFINEMENT USING À-POSTERIORI FINITE ELEMENT ERROR ESTIMATION
Bibliographic record
Abstract
An à-posteriori adaptive estimator is presented and employed for solving viscous incompressible flow problems. In an effort to detect local flow features and resolve flow details, an error estimation that is based on velocity angle is investigated, analyzed and benchmarked by an exact solution which is known as Kovasznay flow. It is found that the estimator is sensitive to the variations of the derivative of the velocity direction field, and it can capture the region and refine grids where the velocity direction has abrupt changes. Unstructured grids are adapted by employing local cell division as well as unrefinement of transition cells. The adaptive scheme is applied to flow over a cavity, flow past a backward-facing step, and flow past an obstacle at different Reynolds numbers. The pressure oscillation which usually occurs in advection-dominated flow cases is suppressed by adding more nodes at the most appropriate regions by using the velocity angle estimator. The results exhibit good accuracy and justify the applicability of the algorithm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".