Flame-Out Detection for Gas Turbine Engines Based Upon Thermocouple Signal Analysis
Bibliographic record
Abstract
This paper describes an experimental study to examine the potential of using Exhaust Gas Temperature (EGT) thermocouples for prompt flame-out detection in gas turbine engines. The approach taken involved accelerating the response of a shielded, slow response thermocouple using electronic processing of the signal. Thus, the abrupt drop in temperature characteristic of a flame-out could be detected within a much shorter time period than would be possible through a conventional thermocouple temperature measurement method. This was intended to provide a robust alternative to existing optical flame-out sensors which have fast response but can be susceptible to false flame-out indications due to window sooting. A production EGT thermocouple with online electronic processing was compared with a production optical flame sensor from a GE F-404 and a laboratory photodiode sensing system. The devices were tested in a full scale GE J-85 combustion chamber sector rig with optical access. The results showed that the thermocouple flame sensor had a response time to flame-outs of less than 100 ms. This was much faster than a conventional thermocouple, but still an order of magnitude longer than the optical flame sensor. However, whereas the optical flame sensor could yield ambiguous results about the presence of flame under some conditions, the thermocouple flame sensor provided a clear indication of flame-out events for all the conditions that were tested.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".