Improving Component Life Prediction Accuracy and Reliability Through Physics Based Prognosis: A Probabilistic Turbine Blade Case Study
Bibliographic record
Abstract
The paper presents the results of a probabilistic creep life study on RRA 501 KB turbine blades and demonstrates the importance of using physics based probabilistic damage modeling techniques to deal with life prediction uncertainty in cast equiaxed components. It is shown that physics based damage analysis yields accurate results and considerably less mechanical properties data is needed for life prediction of cast components. In physics based damage analysis, it is also easy to quickly assess the life limiting damage modes and to establish fracture critical locations in components. In physics based modeling, the influence of individual microstructural or thermal-mechanical loading factors on metallurgical crack nucleation can also be studied with relative ease. Residual life of service exposed parts and effectiveness of life extension techniques can also be predicted because the state of microstructure due to prior service and repair can be taken into account. In this study, Life Prediction Technologies Inc.’s (LPTi’s) prognosis tool known as XactLIFE™ was successfully used to establish the fracture critical location of RRA 501KB first stage gas turbine blades under steady state loads. Deterministic analysis was first used to compute the lower bound airfoil nodal creep life of the various finite element nodes and this was followed by probabilistic creep life analysis to take into account the variability of microstructure from one blade to another. The analysis used typical engine operating data from the field in terms of engine speed and average turbine inlet temperature (TIT). The primary objectives of the case study are to show how prognosis can allow a user to predict component fracture critical locations, establish inspection intervals to avoid failures and establish fleet reliability for engine specific operating conditions.
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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".