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Record W2058428563 · doi:10.2514/1.j052472

High-g Field Combustor of a Rim–Rotor Rotary Ramjet Engine

2014· article· en· W2058428563 on OpenAlexafffund
Mathieu Picard, David Rancourt, Jean‐Sébastien Plante, Martin Brouillette

Bibliographic record

VenueAIAA Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsUniversité de Sherbrooke
FundersNational Research Council Canada
KeywordsRamjetCombustorCombustionIgnition systemMechanicsCombustion chamberAerospace engineeringMaterials scienceAutomotive engineeringAccelerationRotary engineRotor (electric)Mechanical engineeringEngineeringPhysicsChemistryClassical mechanics

Abstract

fetched live from OpenAlex

High- field combustion, such as in rotary ramjet engines, is a promising approach to reduce nitride oxides and combustor size by taking advantage of the flame acceleration due to the buoyancy of the products over the reactants. This paper presents a high- field combustor design for a rim–rotor rotary ramjet engine. In this device, a premixed flow of air and fuel is ignited in the nonrotating inlet track and then swallowed and stabilized in the rotating combustion chamber. Outboard ignition frees the rotating structure from igniters, increasing the maximal tangential speed of the engine and thus its maximal efficiency. The rotating combustor design benefits from extreme centrifugal fields ( to ) for both stabilizing the flame during ignition and maximize flame velocity. A simple buoyancy-driven combustion model allows estimating the combustor length and shows good agreement with numerical simulations, which demonstrate a combustion efficiency to be higher than 85%, even with some reactants bypassing the flameholder. Experiments demonstrate ignition at tangential speeds from 250 to , combustion efficiency from 60 to 75% up to a centrifugal acceleration of , and positive indicated power.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.905

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)0.0010.000

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 head0.006
GPT teacher head0.216
Teacher spread0.209 · 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 designNot applicable
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
Published2014
Admission routes2
Has abstractyes

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