Parallel Implicit Adaptive Mesh Refinement for Unsteady Fully-Compressible Reactive Flows
Bibliographic record
Abstract
An accurate and robust parallel implicit adaptive mesh re nement (AMR) algorithm is proposed and described for the prediction of unsteady behaviour of laminar ames. The scheme is applied to the solution of the system of the partial-di erential equations governing time-dependent, three-dimensional, compressible laminar ows for reactive thermally perfect gaseous mixtures. A high-resolution nite-volume spatial discretization procedure is used to solve the conservation form of these equations on bodytted multi-block hexahedral mesh. A local preconditioning technique is used to remove numerical sti ness and maintain solution accuracy for low-Mach-number, nearly incompressible ows. A exible block-based octree data structure has been developed and is used to facilitate automatic solution-directed mesh adaptation according to physics-based re nement criteria. The data structure also enables an e cient and scalable parallel implementation via domain decomposition. The parallel implicit formulation makes use of a dual-time-stepping like approach with an implicit second-order backward discretization of the physical time, in which a Jacobian-free inexact Newton method with a preconditioned generalized minimal residual (GMRES) algorithm is used to solve the system of nonlinear algebraic equations arising from the temporal and spatial discretization procedures. An additive Schwarz global preconditioner is used in conjunction with block incomplete LU type local preconditioners for each sub-domain. The Schwarz preconditioning and block-based data structure readily allow e cient and scalable parallel implementations of the implicit AMR approach on distributed-memory multi-processor architectures. Numerical results for steady and unsteady laminar coow di usion and premixed methane-air ames demonstrate the capabilities of the proposed approach for a range of reactiveow applications. The scheme is shown to accurately predict key characteristics of the di usion ames. For a premixed ame under terrestrially gravity, the scheme is also shown to accurately predict the frequency of the natural buoyancy induced oscillations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".