Effect of Input Variability on the Performance of Turbine Blade Thermal Design Using Monte Carlo Simulation: An Exploratory Study
Bibliographic record
Abstract
A probabilistic approach to the thermal design and analysis of cooled turbine blades is presented. Various factors that affect the probabilistic performance of the blade thermal design are grouped into categories and a select number of factors known to be significant, for which the variability could be assessed are modeled as random variables. The variability data for these random variables were generated from separate Monte Carlo simulations (MCS) of the combustor and the upstream stator and secondary air system. The oxidation life of the blade is used as a measure to evaluate the thermal design as well as to evaluate validity of the methods. Two approaches have been explored to simulate blade row life variability and compare it with the field data. Field data from several engine removals are used for investigating the approach. Additionally a response surface approximation technique has been explored to expedite the simulation process. The results indicate that the conventional approach of a worst-case analysis is overly conservative and analysis based on nominal values could be very optimistic. The potential of a probabilistic approach in predicting the actual variability of the blade row life is clearly evident in the results. However, the results show that, in order to predict the blade row life variability adequately, it is important to model the operating condition variability. The probabilistic techniques such as MCS could become very practical when approximation techniques such as response surface modeling are used to represent the analytical model.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".