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Record W4231716244 · doi:10.32920/ryerson.14657442.v1

Flight Stability Analysis of a Flexible Rocket Using Finite Elements and Reduced-Order Modeling

2021· preprint· en· W4231716244 on OpenAlexaboutno aff
Kyle Davidson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAerodynamicsFinite element methodAerospace engineeringAerospaceMATLABParametric statisticsFlight dynamicsComputer scienceRocket (weapon)EngineeringStructural engineeringMathematics

Abstract

fetched live from OpenAlex

The coupling of advanced structural and aerodynamic methods is a complex and computationally demanding task. In many cases, simplifications must be made. For the flight simulation of flexible aerospace vehicles, it is common to reduce the overall structure down to a series of linked degenerate structures such as Euler-Bernoulli beams in order to expedite the structural portion of the solution process. The current study employs the sophistication and generality of finite-element based modeling with the concepts of reduced-order modeling to create a general flexible-body flight simulation program. The program was created for use with the MATLAB-Simulink programming package. A parametric analysis on the stability of flexible rockets is performed and results are presented for a variety of rocket configurations based on the SPHADS-1 vehicle under development at Ryerson University. The primary instability mode under study is that associated with the flapping and twisting motions of the tailfins under aerodynamic loading. By varying the average fin thickness, both stable and unstable behaviour is recorded for a variety of flight conditions.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.266
Teacher spread0.222 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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