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Record W2331060728 · doi:10.2514/6.2008-2024

Modeling of an Unconstrained Flexible Rocket Using Finite-Elements and the LISA Method

2008· article· en· W2331060728 on OpenAlexafffundabout
D. J. McTavish, Kyle Davidson, David R. Greatrix

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRocket (weapon)Finite element methodComputer scienceAerospace engineeringEngineeringStructural engineering

Abstract

fetched live from OpenAlex

This paper describes the production of a finite-element based unconstrained motion model for a flexible rocket. The main contribution is a demonstration of the LISA Method, a procedure for transforming a detailed finite-element (small-motion) model directly into a large-motion model with all of its requisite inertia terms. The work is targeted at the problem of modeling flight vehicles, whether aircraft or spacecraft, but is applicable to any large-motion flexible structure modeling application. This methodology promotes a “model it as it is” approach that bypasses the traditional practice of structural simplification to beams or other degenerate structural forms when an unconstrained flexible model is required. The sample problem used to demonstrate the modeling methodology employs the SPHADS-1 rocket vehicle developed at Ryerson University. A source finite-element model of the vehicle was developed in ANSYS and the assembled mass and stiffness matrices exported. The LISA Method was applied to produce a full-detail, full-order large-motion model. To render it more amenable to simulation, a reduced-order dynamics model was then derived from the full-order model. The application of this model in a limited study of aeroelastic stability is described briefly.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score0.229

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.255
Teacher spread0.227 · 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 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
Published2008
Admission routes3
Has abstractyes

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