Laser deposited high temperature thin film sensors for gas turbines
Bibliographic record
Abstract
Purpose Accurate measurements of the temperature distributions in hot section components are indispensable for the prognostic and health management of gas turbines. Thin film thermocouple (TFTC) sensors, directly fabricated on the surface of a component, add negligible mass and create little or no disturbance to airflow, and therefore, can provide accurate measurements of fast temperature fluctuations of gas turbines. The purpose of this paper is to evaluate TFTC sensors fabricated by combining pulsed laser deposition (PLD) and micromachining techniques (LM). Design/methodology/approach The “dry” PLD/LM fabrication approach allows for excellent control of the chemical composition and physical characteristics of the constituent layers and their interfaces, thus achieving good adhesion of the layers to the substrate. Findings The results of thermal cyclic durability testing of the fabricated TFTC sensors demonstrated that the proposed PLD-based approach can be used to fabricate sensors that are fully functional at temperatures up to 750°C. Analyses of the sensor performance during durability testing revealed: the existence of a threshold temperature below which accurate temperature measurements were achieved; an abrupt drop in the sensor output occurring when the sensor temperature exceeded the threshold value, with a fast recovery of the sensor output once the temperature was reduced below the threshold level; and sensor “training” capable of increasing the threshold value of the TFTC through its exposure to above-the-threshold temperatures. Originality/value The work is the first time to demonstrate that simple PLD and LM processes can be used to fabricate TFTC that are fully functional at temperatures up to 750°C.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".