Ensemble Learning Based Surrogate Modeling for Gas Turbine Blisk Temperature Predictions
Bibliographic record
Abstract
Temperature prediction in complex systems like gas turbines provides insights to temperature dependent damage accumulation but usually involves a huge computational cost. For simulation-based prognostics, the computational cost is a major hindrance to a real time implementation. In this work an ensemble learning based multistage surrogate modeling approach is investigated as a possible solution for reducing the computational cost. First the nodal temperature of a turbine blisk is predicted using computational fluid dynamic (CFD) simulations for a limited number of engine operating points. Next the proposed ensemble learning based surrogate modeling approach is implemented to train surrogate models for every node defining the blisk. To achieve computational efficiency, the proposed surrogate modeling framework implements in sequence, clustering techniques for data analysis, multistage polynomial regression modeling, and ensemble learning based model combination. Finally the prediction errors are quantified using the leave-one-out cross-validation method. The result suggests that the computational time could be significantly reduced using the proposed ensemble learning based multistage surrogate modeling technique. The threshold value used to tune the polynomial regression model complexity is also shown to influence the time for surrogate model training.
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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".