Unstructured Anisotropic Mesh Adaptation for Quads Based on a Local Error Model
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-1840.vid This paper presents a method for anisotropic adaptation for all-quad meshes. The goal is to provide a means of reducing the error associated with computational fluid dynamic simulations while limiting the added cost. This is done based on a local error model in order to construct and optimize target element sizes and shapes at each point. A global optimization is then used to find a distribution of degrees of freedom which targets error reductions throughout the computational domain. Additionally, this work will also consider the implementation of a fully unstructured quad mesh generator based on an energy minimization in the anisotropic p norm. This will serve to generate elements matching target sizes and orientations from the aforementioned estimates. Overall, the work will combine these aspects, forming an adaptive framework for use in solving high-order problems with the Discontinuous Galerkin method.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".