Parallel High-Order Anisotropic Block-Based Adaptive Mesh Refinement Finite-Volume Scheme
Bibliographic record
Abstract
A novel parallel, high-order, anisotropic, block-based, adaptive mesh re nement (AMR), nite-volume scheme is proposed and developed herein for the numerical solution of physically complex ow problems having disparate spatial and temporal scales, and with strong anisotropic features. A block-based AMR approach is used which permits highly e cient and scalable implementations on parallel computing architectures and the use of multiblock, bodytted computational grids for the treatment of complex geometries. Rather than adopting a more usual isotropic approach to the re nement of the grid blocks, the proposed approach uses a binary hierarchical tree data structure that allows for anisotropic re nement of the grid blocks in each of the coordinate directions in an independent fashion. This allows for the more e cient and accurate treatment of narrow layers, discontinuities, and/or shocks in the solutions which occur, for example, in the thin boundaries and mixing layers of high-Reynolds-number viscous ows and in the regions of strong non-linear wave interactions of high-speed compressible ows with shocks. The anisotropic AMR approach is combined with a computationally e cient, high-order, central, essentially non-oscillatory (CENO), cell-centered, upwind, nite-volume scheme and an e cient parallel, implicit and explicit scheme for the solution of general systems of partial di erential equations governing both steady and time-varying problems. The CENO upwind scheme makes use of Riemann-solver based ux functions and a solution smoothness indicator to provide robust, accurate, and monotonic treatment of shocks and under-resolved solution content. The nite-volume scheme is applied to the solution of both a model system, the advectiondi usion equation, as well as the Euler equations governing compressible, inviscid, gaseous ows in two space dimensions. The potential of the parallel adaptive scheme for dealing with ows having disparate scales is clearly demonstrated.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 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".