Design and Validation of an Experiment for the Detection and Prediction of Stall and Surge in a PT6/T400 Turboshaft Engine
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".