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Record W4245158397 · doi:10.32920/ryerson.14644173

Hybrid Rocket Engine Research In Support Of Prototype Development & Testing

2021· preprint· en· W4245158397 on OpenAlexaffabout
Adam P. Trumpour

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNozzleSpecific impulseInjectorRocket enginePropellantMechanical engineeringPropulsionThrustLiquid-propellant rocketBody orificeAerospace engineeringAutomotive engineeringEngineeringNuclear engineering

Abstract

fetched live from OpenAlex

This thesis gives a detailed overview of the design of a small hybrid rocket engine (HRE) that is being built in support of propulsion research activities at Ryerson University, and examines the various research-related issues surrounding the operation and performance of an HRE. The engine design work is undertaken with gaseous oxygen (GOX) as the oxidizer and paraffin and polyethylene as the intended fuels, but the system is readily adaptable to other propellant combinations for future investigations. Particular emphasis was placed on the design and analysis of the GOX injector and the nozzle. The injector orifice design was supported by CFD analysis as well as cold-flow testing. The nozzle was initially designed and fabricated from graphite, but a water-cooled copper design is also considered for those occasions when a fully reusable, non-eroding nozzle is desirable for accurate engine performance measurements. The preliminary nozzle design is supported by a steady-state CFD analysis of the nozzle flows and associated heat fluxes. The overall engine design is further evaluated by examination of internal ballistic simulation results, with respect to such factors as expected performance (chamber pressure, thrust, specific and total impulse) for a given oxidizer mass flow rate and nozzle throat size. The requirements for expected future studies, such as for evaluating engine operation and performance below and above the stoichiometric length of the given engine, have been incorporated where possible into the present design.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.221
GPT teacher head0.380
Teacher spread0.159 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same topicRocket and propulsion systems researchFrench-language works237,207