A parallel adaptive-mesh refinement scheme for predicting laminar diffusion flames
Bibliographic record
Abstract
A parallel block-based adaptive mesh refinement (AMR) scheme is described and applied to the prediction of the structure of non-premixed axisymmetric methane-air laminar diffusion flames. The parallel solution-adaptive algorithm solves the system of partial-differential equations governing two-dimensional axisymmetric compressible laminar flows for reactive thermally perfect gaseous mixtures. A finite-volume spatial discretization procedure is used to solve the equations on a body-fitted multi-block quadrilateral mesh. Limited piecewise linear solution reconstruction and Riemann solver based flux functions are used to evaluate the inviscid fluxes and a centrally weighted second-order discretization procedure is adopted for determining the viscous fluxes. A flexible block-based hierarchical data structure facilitates mesh adaptation, and enables efficient and scalable implementations of the algorithm on multi-processor architectures via domain decomposition. Numerical results are discussed for a co-flow laminar diffusion flame demonstrating the validity of the parallel AMR approach and the ability of the mesh adaption scheme to resolve fine-scale features of the solution.
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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 | 0.000 |
| 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 | 0.001 | 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".