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Record W2110085262 · doi:10.2514/6.2004-4179

Transient Chamber Flowfield Simulation of a Rod-and-Tube Configuration Solid Rocket Motor

2004· article· en· W2110085262 on OpenAlexaff
John Weaver, Jérôme Gauthier, Robert Stowe

Bibliographic record

Venue40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit · 2004
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsDefence Research and Development CanadaCarleton University
Fundersnot available
KeywordsSolid-fuel rocketTransient (computer programming)MechanicsTube (container)Rocket (weapon)Transient analysisAerospace engineeringMaterials sciencePhysicsMechanical engineeringTransient responseEngineeringComputer sciencePropellantElectrical engineering

Abstract

fetched live from OpenAlex

A transient CFD simulation of the flowfield within a rod-and-tube solid propellant rocket motor has been developed. This model couples the fluid dynamics and heat transfer of the gas flowfield within the rocket port to the nozzle to predict the internal environment within the motor including the regression rate of the propellant. The propellant regression is described with an empirical erosive burning model based on the phenomenological heat transfer approach derived by Lenoir and Robillard 1 . The predicted propellant burn rate and consequently the chamber pressure were found to be significantly increased from the case where propellant regression was described by the simple burning law only. This augmentation of the burn rate, particularly during the early stages of the simulation, was in agreement with the trends observed in small diameter rockets where erosive burning was present. A validation of the model comparing an actual Pressure - Time plot to that predicted by the CFD model was also carried out and achieved a very high degree of correlation.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.275
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations3
Published2004
Admission routes1
Has abstractyes

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