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Record W4244042811 · doi:10.32920/ryerson.14646417

Comparison Of Solid Rocket Motor Thrust Modulation Technique

2021· preprint· en· W4244042811 on OpenAlexaff
Clayton Edward Wozney

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsToronto Metropolitan UniversityBrandon University
Fundersnot available
KeywordsThrustPropellantChamber pressureNozzleSolid-fuel rocketAerospace engineeringRocket engine nozzleCombustion chamberRocket (weapon)PropulsionAccelerationMechanical engineeringMaterials scienceEngineeringPhysicsMechanicsCombustionChemistryClassical mechanics

Abstract

fetched live from OpenAlex

The thrust profiles of solid rocket motors are usually determined ahead of time by propellant composition and grain design. Traditional techniques for active thrust modulation use a moveable pintle to dynamically change the nozzle throat diameter, increasing the chamber pressure and therefore thrust. With this approach, high chamber pressures must be endured with only modest increases in thrust. Alternatively, it has been shown that spinning a solid rocket motor on its longitudinal axis can increase the burning rate of the propellant and therefore the thrust without the resulting high chamber pressures. Building on prior experience modelling pressure-dependent, low-dependent and acceleration-dependent burning in solid rocket motors, an internal ballistic simulation computer program is modified for the present study where the use of the pintle nozzle and spin-augmented solid rocket motor combustion approaches, for a reference cylindrical-grain motor, are compared. This study confirms that comparable thrust augmentation can be gained at lower chamber pressures using the novel spin-acceleration approach, relative to the established pintle-nozzle approach, thus potentially providing a significant design advantage.

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 score0.331
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.366
Teacher spread0.304 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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