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Record W3203222894 · doi:10.2514/1.b38330

Hybrid Rocket Engine Performance Assessment Using Plume Luminosity Oscillations

2021· article· en· W3203222894 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Propulsion and Power · 2021
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRocket (weapon)PlumeRocket engineSolid-fuel rocketAerospace engineeringLiquid-propellant rocketCombustionInstabilityLuminosityAcousticsCharacteristic velocityMechanicsPhysicsPropellantEngineeringMeteorologyAstrophysics

Abstract

fetched live from OpenAlex

Spectral analyses were performed on high-speed video of three nitrous-oxide/paraffin-based hybrid rocket launches and one static test fire of a nitrous/paraffin-based hybrid rocket motor. The imagery was taken to assess the combustion stability and identify any dominant instability modes. A spectral analysis of the image luminosity signals has shown distinct oscillatory modes in the plume flowfield. The plume oscillations have been compared and linked to existing hybrid combustion instabilities reported in the literature and theoretical predictions. The use of high-speed imagery has proven useful as a nonintrusive method of gathering high-frequency data in the analysis of hybrid combustion during launch. Analysis of high-speed imagery from a static test has revealed the time-varying aspect of the dominant oscillatory modes. The first longitudinal acoustic mode, identified in all data sets, has been used in a novel way to determine the characteristic velocity of the operating motor.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.384

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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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