Numerical assessment of blade deflection and elongation for improved monitoring of blade and TBC damage
Bibliographic record
Abstract
Abstract The reliability of turbine blades is largely maintained by damage tolerance approach based on monitoring and pre-set periodic inspections. This can result in unnecessary downtimes, premature part retirement and unforeseeable failures. Therefore, there is growing interest in systems that can reliably detect damages in real‐time. However, many current sensors are based on blade tip clearance and time of arrival. The first primarily correlates with relatively predictable long-term creep deformation and ensuing blade elongation, while the second can be related to blade deflection. Therefore, this research comparatively assesses the two parameters. For this purpose, TBC defects, representative for coating spallation, and notches, representative for blunted blade cracks, are investigated. Overall, the results suggest that the measurement of changes in axial deflection could show higher sensitivity to cracks and TBC defects, and therefore, constitutes a potential alternative for continuous monitoring with respect to unforeseeable rapidly growing blade damage. Moreover, TBC spallation seems more difficult to immediately detect as the ensuing changes in blade tip position are small. However, they cause changes in deflection that can switch from negative to positive as they are located closer to the blade root, which may allow to assess their location during monitoring. In contrast, critical cracks located close to the blade root can cause measurable changes in blade deflexion, potentially making their timely detection and continuous monitoring possible.
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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 | 0.000 |
| Open science | 0.000 | 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".