Entropy-Adjoint p-Adaptive Discontinuous Galerkin Method for the Under-Resolved Simulation of Turbulent Flows
Bibliographic record
Abstract
This paper presents an approach to mesh adaptation suitable for scale-resolving simulations. The methodology is based on the entropy-adjoint approach, which corresponds to a standard output-based adjoint method with the output functional targeting areas of spurious generation of entropy. The method shows several advantages over standard output-based error estimation: 1) it is computationally inexpensive; 2) it does not require the solution of a fine-space adjoint problem; and 3) it is nonlinearly stable with respect to the primal solution for chaotic dynamic systems. In addition, the work reports on the parallel efficiency of the solver, which has been optimized through a multiconstraint domain decomposition algorithm available within the Metis 5.0 library. The reliability, accuracy, and efficiency of the approach are assessed by computing three test cases: the two-dimensional, laminar, chaotic flow around a square at ; and the implicit large-eddy simulation of the flow past a circular cylinder at and past a square cylinder at . The results show a significant reduction in the number of degrees of freedom with respect to uniform order refinement with a good agreement with experimental data.
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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) | 0.000 | 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) | 0.001 | 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".