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Record W2324381607 · doi:10.2514/6.2014-3479

The VINCI Engine Vibration Test campaign

2014· article· en· W2324381607 on OpenAlexaff
Arnaud Sternchüss, Alexina Bossaert, Patrick Manfredi, Noel David, Patrick Alliot, Anton Grillenbeck

Bibliographic record

Venue50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsTest (biology)VibrationAutomotive engineeringComputer scienceEngineeringAeronauticsAcousticsPhysicsGeology

Abstract

fetched live from OpenAlex

The VINCI® Engine is the newly developed expander-cycle engine for future European launcher upper stages. This paper takes place in the series of AIAA publications that started on the early VINCI® Engine design. The present paper reports on the engine dynamic characterization which constitutes a major milestone before entering the qualification phase of the engine. This presentation details the present engine development status and the chosen dynamic characterization approach with respect to the programmatic objectives. Specific aspects of the planning and preparation work for the engine vibration test campaign are addressed. Finally, main results and their use for the forthcoming engine qualification activities are highlighted.

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)0.002
metaresearch head score (Gemma)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.238
Teacher spread0.213 · 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 designOther design
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
Published2014
Admission routes1
Has abstractyes

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