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Record W2997682143 · doi:10.2514/6.2020-1603

Modular Derivation of the Equations of Motion of a Flexible Launch Vehicle with Propellant Slosh

2020· article· en· W2997682143 on OpenAlexaff
Charles Champagne Cossette, James Richard Forbes, David Saussié

Bibliographic record

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsPolytechnique MontréalMcGill University
Fundersnot available
KeywordsSlosh dynamicsPropellantModular designTrajectoryPropulsionVehicle dynamicsAerospace engineeringEquations of motionComputer scienceMissileLaunch vehicleVariety (cybernetics)EngineeringControl theory (sociology)Control engineeringSimulationControl (management)Physics

Abstract

fetched live from OpenAlex

Thoroughly assessing the performance of launch vehicle control systems often requires the use of high-fidelity simulation tools. In particular, launch vehicle control systems must take into consideration the effects of propellant slosh as well as a flexible structure in order to mitigate oscillations and follow a desired trajectory. This paper presents a method for deriving the equations of motion that couple the rigid-body, slosh, and flexible dynamics in an accessible and modular way. The formulation is generalized to an arbitrary number of propellant tanks and flexible modes, thereby being applicable to a variety of launch vehicles. The paper concludes with a numerical simulation of an example launch vehicle, showing the interaction of the different dynamics.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.193
Teacher spread0.184 · 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
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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