Challenges in life prediction of gas turbine critical components
Bibliographic record
Abstract
Life prediction methods have been evolving for many decades. Application of these methods is very important for economic operation of gas turbine engine fleets. The challenges in life prediction for gas turbine components arise due to their severe operating environments, such as high-temperature environments and corrosive-erosive high-speed gaseous environments. The combination of mechanical and thermal loads often induces low-cycle fatigue and creep damage in components. Therefore, in component life prediction analyses, (i) realistic constitutive laws must be employed, and (ii) thermomechanical fatigue or interactions of creep-fatigue must be considered, in addition to an accurate description of the loads and boundary conditions. Challenges also lie in the validation of the theoretical life predictions and life updates. This paper briefly reports the recent advances at the National Research Council of Canada Institute for Aerospace Research (NRC-IAR) regarding the life prediction aspects and discusses contemporary methods, opportunities, and challenges in life prediction and life update for critical components of gas turbine engines using case studies. The emphasis is on prediction of crack nucleation and crack growth life using physics-based modelling and numerical analyses. In addition, methods and results of component testing are summarized.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".