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Record W4200462388 · doi:10.1134/s1990793121030301

Comparative Analysis of the Detonation Combustion of Kerosene and Gasoline Vapors in a Laval Nozzle

2021· article· en· W4200462388 on OpenAlexaboutno aff
Yu. V. Tunik, G. Ya. Gerasimov, V. Yu. Levashov

Bibliographic record

VenueRussian Journal of Physical Chemistry B · 2021
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsDetonationMechanicsNozzleCombustionGasolineCylinderKeroseneRamjetMach numberThrustKinetic energyChemistryThermodynamicsMaterials scienceCombustorPhysicsClassical mechanicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The possibility of stabilizing the detonation combustion of kerosene and gasoline vapors in a supersonic air flow entering an axisymmetric convergent–divergent nozzle with a central body under atmospheric conditions at an altitude of 16 km is studied numerically. The central body provides direct initiation of detonation due to the thermal and kinetic energy of the incident flow. The mathematical model is based on two-dimensional unsteady Euler equations for an axisymmetric flow of a multicomponent reacting gas and reduced kinetic models of combustion of flammable mixtures. The calculations use a modification of the numerical scheme of S.K. Godunov of the second order of accuracy in spatial variables. The central body of the cylinder–cone (CC) and cone–cylinder–cone (CCC) types is considered. The possibility of stabilizing the detonation combustion of kerosene at the oncoming flow of Mach numbers of M = 7 and 9 with thrust generation is shown. In the case of gasoline, only a small part of the mixture burns in the detonation mode behind the detached shock wave in front of the end wall of the central body. The thrust obtained in gasoline vapors does not compensate the aerodynamic resistance of the nozzle and the central body.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.197

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 science0.0000.000
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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 designBench or experimental
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

Citations4
Published2021
Admission routes1
Has abstractyes

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