A conforming sliding mesh technique for an embedded‐hybridized discontinuous Galerkin discretization for fluid‐rigid body interaction
Bibliographic record
Abstract
Abstract In previous work, we introduced a space‐time embedded‐hybridizable discontinuous Galerkin method for the solution of the incompressible Navier–Stokes equations on time‐dependent domains of which the motion of the domain is prescribed. This discretization is exactly mass conserving, locally momentum conserving, and energy‐stable. In this article, we extend this discretization to fluid‐rigid body interaction problems in which the motion of the fluid domain is not known a priori. To account for large rotational motion of the rigid body, we present a novel conforming space‐time sliding mesh technique. For this we introduce a local edge swapping algorithm such that the global mesh of a space‐time slab is one of only four different configurations. These configurations can be pre‐computed thereby reducing any costs associated with changing mesh connectivity. Furthermore, edge swapping occurs only within the space‐time slab between the discrete time‐levels; there is no edge swapping on spatial meshes and so there is no need to project the solution from one spatial mesh to another. We demonstrate the performance of the discretization on various numerical examples.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".