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Record W4225905900 · doi:10.2514/1.b38659

Evaluation of a Paraffin/Nitrous Oxide Hybrid Rocket Motor with a Passive Mixing Device

2022· article· en· W4225905900 on OpenAlexafffund
Colin Hill, Will Nelson, Craig T. Johansen

Bibliographic record

VenueJournal of Propulsion and Power · 2022
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsUniversity of Calgary
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMixing (physics)CombustionMaterials scienceBaffleNitrous oxidePropellantCombustion chamberMass fluxWork (physics)MechanicsNuclear engineeringAutomotive engineeringEnvironmental scienceAerospace engineeringChemistryThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Hybrid rockets offer numerous advantages over other types of rockets but tend to suffer from low combustion efficiency. Higher efficiency can be achieved by using passive devices in the combustion chamber that promote additional mixing of unreacted propellants. The current work examines a paraffin and nitrous oxide hybrid motor that uses a mixing device located downstream of the fuel grain to enhance combustion efficiency. The mixing device used in this work is a six-port baffle plate. Combustion efficiency, as measured by characteristic velocity, was found to increase by over 40% with the use of the passive mixing device. It was identified that certain high-mass-flux motor configurations resulted in unstable motor operation when the mixing device was present. Experimental results indicate that stable combustion occurs for oxidizer mass flux levels below . Exceeding this limit results in combustion oscillations in excess of 20% of the mean chamber pressure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.274
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2022
Admission routes2
Has abstractyes

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