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Record W4239246954 · doi:10.32920/ryerson.14652105.v1

Fluid-structure interaction considerations for solid rocket motor internal ballistics modeling

2021· preprint· en· W4239246954 on OpenAlexaff
Giovanni Montesano

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInternal ballisticsPropellantCombustionMechanicsSolid-fuel rocketRocket (weapon)InstabilityCombustion chamberInternal flowBallisticsFinite element methodAerospace engineeringFlow (mathematics)PhysicsMaterials scienceEngineeringThermodynamicsChemistry

Abstract

fetched live from OpenAlex

A study of the numerical modeling and prediction of nonlinear unsteady combustion instability within the combustion chamber of a solid rocket motor (SRM) is the main objective. The numerical model consists of a three-dimensional finite-element representation of a cylindrical-grain motor, coupled to a quasi-one-dimensional internal ballistic flow (IBF) model, where a quasi-steady rapid kinetic rate burning rate algorithm is used to model the propellant combustion and regression. Fluid-structure-combustion interaction subroutines are also employed to control the simulated motor firings and the data transferred between the fluid, structure and burning rate model components. Results illustrating the significant effects of structural vibrations on the burning rate and consequently the IBF are shown and compared to experimental data. Modeling considerations are illustrated, giving insight into the physical phenomena of SRM combustion instability.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score1.000

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 communication0.0010.000
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 head0.069
GPT teacher head0.334
Teacher spread0.265 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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