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Record W2039854410 · doi:10.1115/gt2007-28244

Design and Validation of an Experiment for the Detection and Prediction of Stall and Surge in a PT6/T400 Turboshaft Engine

2007· article· en· W2039854410 on OpenAlexaff
Jennifer L. Chalmers, Jeff Bird, Donald Gauthier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsStall (fluid mechanics)Gas compressorSurgeCentrifugal compressorAxial compressorEngineeringAerodynamicsAutomotive engineeringReciprocating compressorComputer scienceAerospace engineeringElectrical engineering

Abstract

fetched live from OpenAlex

The aerospace industry is aggressively pursuing many avenues of engine health monitoring to improve aircraft safety and reduce operating cost. A PT6/T400 turboshaft engine has been instrumented specifically to determine if measurable compressor aerodynamic behavior can provide a warning of impending stall or surge, especially in a small (< 5kg/s), service-exposed, axi-centrifugal compressor. In accordance with a survey of experience and methods for stall testing and detection methods, the engine was instrumented with nine fast-response pressure transducers (monitored at 25 kHz) divided between the axial compressor first stage leading edge, the axial compressor exit, and the outlet of the centrifugal compressor diffuser. An automatic bleed valve was gradually disabled to induce compressor stall. The engine response to this gradual change corresponded to the predictions of a simple engine surge model. A technique for monitoring blade air-flow regularity was developed and used to prove that aerodynamic changes could be successfully detected before the onset of stall/surge. The new technique compared favorably to conventional time series analysis, fast Fourier transform and wavelet processing techniques. Recommendations are made for further improvements and study of test and analysis methods.

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: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.165

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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