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Record W2333825495 · doi:10.2514/6.2013-2433

Parallel Implicit Adaptive Mesh Refinement for Unsteady Fully-Compressible Reactive Flows

2013· article· en· W2333825495 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue21st AIAA Computational Fluid Dynamics Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoCompute Canada
KeywordsAdaptive mesh refinementComputer scienceCompressibilityMesh generationComputational scienceMechanicsFinite element methodPhysicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.225
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it