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Record W4292585425 · doi:10.48550/arxiv.1312.1890

Influence of hydrodynamic instabilities on the propagation mechanism of\n fast flames

2013· preprint· W4292585425 on OpenAlexaff
Logan Maley, Rohit Bhattacharjee, S. She-Ming Lau-Chapdelaine, Matei I. Radulescu

Bibliographic record

VenuearXiv (Cornell University) · 2013
Typepreprint
Language
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDetonationMechanicsMach numberSupersonic speedShock waveInstabilityTurbulenceHydrogenShock (circulatory)Shock diamondVorticityDeflagration to detonation transitionMethanePhysicsThermodynamicsExplosive materialMaterials scienceChemistryMach waveVortex

Abstract

fetched live from OpenAlex

The present work investigates the structure of fast supersonic turbulent\nflames typically observed as precursors to the onset of detonation. These high\nspeed deflagrations are obtained after the interaction of a detonation wave\nwith cylindrical obstacles. Two mixtures having the same propensity for local\nhot spot formation were considered, namely hydrogen-oxygen and methane-oxygen.\nIt was shown that the methane mixture sustained turbulent fast flames, while\nthe hydrogen mixture did not. Detailed high speed visualizations of nearly\ntwo-dimensional flow fields permitted to identify the key mechanism involved.\nThe strong vorticity generation associated with shock reflections in methane\npermitted to drive jets. These provided local enhancement of mixing rates,\nsustenance of pressure waves, organization of the front in stronger fewer modes\nand eventually the transition to detonation. In the hydrogen system, for\nsimilar thermo-chemical parameters, the absence of these jets did not permit to\nestablish such fast flames. This jetting slip line instability in shock\nreflections (and lack thereof in hydrogen) was correlated with the value of the\nisentropic exponent and its control of Mach shock jetting instability (Mach &\nRadulescu).\n

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 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: Empirical
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.156
Teacher spread0.129 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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