Application of Surrogate Models and Probabilistic Design Methodology to Assess Creep Growth Limit of an Uncooled Turbine Blade
Bibliographic record
Abstract
Predictive lifing with probabilistic treatment of key variables represents a promising approach to realizing the digital gas turbine of the future. In this paper, we present a predictive model for creep life assessment of an uncooled turbine blade. The model development methodology draws on well-established machine learning principles to develop and validate a surrogate model for creep life from engine performance parameters. Verified creep life results, obtained from 3D non-linear thermo-mechanical finite element simulation for varying engine operating conditions are used as the basis for model development. The selection of model response surface order is studied over a range of models by evaluating normalized residual error on training and uncorrelated validation data sets. A model that is fully quadratic in the data set features is shown to have excellent predictive capability, yielding nominal creep life predictions to within ± 3% on the validation data set. This work then considers probabilistic techniques to evaluate the impact of uncertainty associated with each key factor on the predicted nominal creep life in order to achieve a mandated life target with a defined probability of failure.
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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".