Residual Life Assessment of a Critical Component of a Gas Turbine: Achievements and Challenges
Bibliographic record
Abstract
The residual life assessment of a turbine spacer from a gas turbine engine is presented. The spacer has been identified as one of the safety critical components of the engine, therefore the useful life of this component significantly affects economic operation of the fleet. Numerical analyses of fatigue crack propagation at one critical location of the spacer were performed using both three dimensional (3D) finite element based method and the weight function method. These results combined with the material data allowed for basic assessment of the damage tolerance of this component. Experimental validation of the spacer life was performed in a spin rig facility. During this validation, two sets of spacers were tested and the number of cycles to appearance of a detectable crack was recorded. Moreover, a fractographic study was conducted on the fracture surfaces of two spin rig tested spacers using scanning electronic microscopy techniques. It was found that crack nucleation occurred at multiple sites and crack propagation occurred by a mixed mode of striation formation and faceted fracture. Therefore it was concluded that the mixed mode interaction should be considered in predicting the fatigue life of the spacer. Finally, the paper describes the challenges and pitfalls encountered during preparation and execution of the analyses and tests, including availability of engine and operational data and also uncertainties in interpretation of the results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".