Experimental evaluation of fs-IR FBG sensors for application in gas turbine temperature measurement
Bibliographic record
Abstract
Gas turbine engines produce thrust or power by expanding hot gases generated by burning fuel. Therefore, it is very important to measure and monitor temperature accurately in order to control and protect the engine. In particular, for advanced gas turbine engines, accurate temperature measurement in hot sections such as the combustor and turbine become increasingly important since demands for higher efficiency and more stringent emission targets drive their operation closer and closer to established limits. The ability to measure temperature under such harsh environments is currently restrained by the lack of sensors and controls capable of withstanding the high temperature, pressure and corrosive conditions present. Thermocouples are most commonly used due to their low cost and simplicity, but where steep temperature gradients exist, their slow response and instrumentation complexity demand alternative solutions. Femtosecond infrared (fs-IR) written fiber Bragg grating (FBG) sensors have inherent advantages over thermocouples for rapidly measuring high resolution temperature profiles under harsh conditions. Perhaps the greatest benefit of fs-IR FBG sensors is their quasi-distributed sensing capability, with many sensors deployable in such environments, along a single optical fiber filament. This paper presents the results of experimental studies to compare the measurement performance between a thermocouple rake and fs-IR FBG temperature probes for the combusted gas temperature at the combustor exit plane which has stiff temperature gradients. In addition, preliminary results of wall temperature measurement using fs-IR FBG sensors at the combustor wall will be presented. Along with the experimental comparison, this paper will include the pros and cons of FBG sensors, discussion of deployment strategies, as well as comments on reliability and other important considerations.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".