Anisotropic Non-Uniform Block-Based Adaptive Mesh Refinement for Three-Dimensional Inviscid and Viscous Flows
Bibliographic record
Abstract
A parallel anisotropic block-based adaptive mesh refinement (AMR) algorithm is proposed to describe the solution of physically complex flow problems with disparate spatial and temporal scales exhibiting highly anisotropic features on three-dimensional multi-block body-fitted hexahedral meshes with non-uniform grid blocks. Instead of using a classical uniform treatment for the computational cells of each block within the multi-block grids, the proposed anisotropic AMR scheme adopts a non-uniform representation of the cells within each block. With the former approach, all of the cells for a given block are forced to be at the same resolution, including both interior and ghost cells containing solution information from neighboring blocks. In such a uniform representation, various techniques are required to evaluate the solution in the ghost cells and ensure flux conservation at block interfaces with such a uniform representation. The proposed non-uniform approach directly uses the neighboring cells as the ghost cells, even at a grid resolution change, and this affords a number of computational advantages. A modified upwind finite-volume spatial discretization scheme is applied in conjunction with the AMR scheme to the solution of Euler and Navier-Stokes equations for inviscid and viscous compressible gaseous flow. Steady-state and time-varying flow problems are considered on anisotropic adapted meshes. The potential flexibility and efficiency of this enhanced anisotropic AMR scheme are demonstrated for the simulation of flows of varying complexity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".