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Record W7015602796

Turboshaft engine performance comparison between CVT and fixed ratio transmission for a variable speed rotor helicopter

2013· article· en· W7015602796 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Padua Archive (University of Padua) · 2013
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
Fundersnot available
KeywordsAirframeContinuously variable transmissionRotor (electric)Rotational speedTurbineControl theory (sociology)Variable (mathematics)Power (physics)Propulsion
DOInot available

Abstract

fetched live from OpenAlex

In a usual turboshaft engine for helicopter applications, the free power turbine (FPT) rotational speed is normally governed ensuring that the rotor speed is as constant as possible for each type of flight maneuver. The main reason for choosing a constant rotational speed is linked to the supposed decrease in engine efficiency at part load (typical variations in speed do not exceed 15% [1]), as well as because variable speed drives trains of resonant frequencies into the airframe [2]. However, recent studies [3],[4] and real implementations bringing up composites into the airframe structure make it possible to successfully accommodate varying speed rotors without hitting resonant frequencies and with even better engine performance [3]. A recent example is the variable speed Pratt & Whitney Canada PW207D turboshaft, a single-stage shrouded power turbine which powers Boeing's A160 Hummingbird and Bell Helicopter's Eagle Eye UAV.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.257
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 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
Published2013
Admission routes1
Has abstractyes

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