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Record W2098678984 · doi:10.1098/rspa.2008.0371

Numerical simulation of detonation structures using a thermodynamically consistent and fully conservative reactive flow model for multi-component computations

2009· article· en· W2098678984 on OpenAlexaff
Giuki Cael, Hoi Dick Ng, Kevin R. Bates, Mark Short

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2009
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsConcordia University
Fundersnot available
KeywordsDetonationFinite volume methodComputationFlow (mathematics)Component (thermodynamics)MechanicsComputer simulationStability (learning theory)Applied mathematicsThermodynamicsComputer scienceChemistryPhysicsMathematicsExplosive materialAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a simplified reactive multi-gas model for the numerical simulation of detonation waves. The mathematical model is formulated based on a thermodynamically consistent and fully conservative formulation, and is extended to model reactive flow by considering the reactant and product gases as two constituents of the system and modelling the conversion between these by a simple one-step reaction mechanism. This simplified model allows simulations using more appropriate chemico-thermodynamic properties of the combustible mixture and yields close Chapman–Jouguet detonation parameters from detailed chemistry. The governing equations are approximated using a high-resolution finite volume centred scheme in an adaptive mesh refinement code, permitting high-resolution simulations to be performed at flow regions of interest. The algorithm is tested and validated by comparing results to predictions of the one-dimensional linear stability analysis of the steady detonation and through the study of the evolution of two-dimensional cellular detonation waves in gaseous hydrogen-based mixtures.

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.

How this classification was reachedexpand

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 categoriesnone
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 score0.411
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.267
Teacher spread0.237 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2009
Admission routes1
Has abstractyes

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