An Innovative Non-Destructive Method for the In-Service Metal Surface Temperature Estimation of Coated GT Parts
Bibliographic record
Abstract
The exact knowledge of the actual airfoil surface metal temperature of advanced GT parts during engine operation is essential with regard to a reliable part lifetime analysis. Typically, in order to assess the airfoil temperatures of service exposed parts, destructive metallographical methods are used, which interpret the superalloy and coating degradation into respective metal temperatures. This traditional metallographical method is well established, however, it provides only limited information on the metal temperatures from few specific cutting locations. This paper describes the development of an innovative approach for the determination of the metal surface temperatures of ex-service turbine blades & vanes using the non-destructive Frequency Scanning Eddy Current Technique (FSECT). The method is basically based on the fact, that MCrAlY coatings, in comparison to the base superalloy material, change their electromagnetic properties in a describable manner with temperature and time. The coated GT part needs to be FSECT measured in the status “ex-service” and after defined reference heat treatments. The respective FSECT signals are then used in order to calculate the actual metal surface temperature. With this method and using an automated robotic system, it is possible to establish accurate and high resolution mappings of the surface temperature distribution over the entire airfoil and platform areas. The exact location of hot spots on the airfoil, which might not be evident with the naked eye, can easily be identified. This new method supports the prediction of local material degradation and lifetime, the optimization of maintenance intervals, and — as a consequence — the establishment of the appropriate reconditioning processes for the ex-service GT parts.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".