A New Scaling Method for Component Maps of Gas Turbine Using System Identification
Bibliographic record
Abstract
A scaling method for characteristics of gas turbine components using experimental data or partially given data from engine manufacturers was newly proposed. In case of currently used traditional scaling methods, the predicted performance around the on-design point may be well agreed with the real engine performance, but the simulated performance at off-design points far away from the on-design point may not be well agreed with the real engine performance generally. It would be caused that component scaling factors, which were obtained at on-design point, is also used at all other operating points and components’ maps are derived from different known engine components. Therefore to minimize the analyzed performance error in the this study, first components’ maps are constructed by identifying performances given by engine manufacturers at some operating conditions, then the simulated performance using the identified maps is compared with performances using currently used scaling methods. In comparison, the analyzed performance by the currently used traditional scaling method was well agreed with the real engine performance at on-design point but had maximum 22% error at off design points within the flight envelope of a study turboprop engine. However, the performance result by the newly proposed scaling method in this study had maximum 6% reasonable error even at all flight envelope.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".