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Record W2125860289 · doi:10.2514/6.2008-4184

ADVACT: A European Programme Investigating Adaptive Technologies for Future Aero Gas Turbine Engines

2008· article· en· W2125860289 on OpenAlexaff
Cosimo Buffone, John R. P. Webster, Vasileios Kyritsis, Nicolas Evanno, Sven Hillel, Philippe Pernord, Alain Merlen, Vladimir Preobrazhensky, Philippe Chanez, Éric Garnier, Christian Wakelam, Simon Evans, Andrea Tonoli, Mario Silvagni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsGas turbinesAero engineComputer scienceAerospace engineeringAeronauticsEnvironmental scienceSystems engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

*† ‡ § ** †† ‡‡ §§ , *** ††† , ‡‡‡ §§§ **** This paper will give an overview of flow-actuation systems contemplated in the ADVACT project, a European programme funded under the 6th Framework Programme. The project addresses the application of actuator technology in an aircraft engine environment, in particular flow-control of intake under strong cross-wind conditions, flow-control of low and high speed cascade blades and variable area nozzles by Shape Memory Alloys. The primary objective of ADVACT is to enable the achievements of improvements in operation, costs and reduction of environmental impact of gas turbines by the provision of extended in-flight actuation and control of engine parameters. Extended simulation work along with both material and devices characterization has enabled the ADVACT Consortium to design appropriate actuation systems that are aimed at engineconditions. Simulation and device characterization are the primary objects of this paper along with some insight on construction of rigs for testing under engine like conditions; this latter being the object of future work within ADVACT. Engine performance analysis with the adoption of some of these advanced actuation techniques is also investigated and data on 2 and 3-shaft engines are presented.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.844

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 head0.017
GPT teacher head0.191
Teacher spread0.175 · 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 routes1
Has abstractyes

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