Blisk Life Prediction for Twin-Shaft Turbo Engines by Using a Two-Dimensional Thermal Off-Design Model
Bibliographic record
Abstract
Understanding the off-design engine behavior is very important for the life cycle management of gas turbines because overhaul interval must be predicted accurately to minimize engine down time and maintenance costs. An off-design thermodynamic model has been developed for twin-shaft engines to obtain mean-line temperature distribution to be used in a prognostics system. A semi-empirical projecting approach was adopted to compute the two-dimensional temperature distribution for the gas path components. This temperature profile was then fed into the structural analysis and damage modeling modules of the prognostics system to predict the fracture critical location and crack initiation life of an A 250 integrally cast bladed disc (blisk). Comparison of the twin shaft model with GasTurb results showed that the model predicts the temperature profile within +/− 8K. The life prediction results from the prognostics system also match closely with the field data.
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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 | 0.001 | 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".