A Simple Sub-Idle Component Map Extrapolation Method
Bibliographic record
Abstract
This paper describes a simple sub-idle component map extrapolation method. Used in conjunction with gas turbine performance models, it enables designers to estimate sub-idle gas turbine performance during engine start-up. The lack of information available regarding component maps in the sub-idle regime creates major challenges for starting system designers or control system designers as the numerical convergence of performance models decreases rapidly below idle speed. The proposed component map extrapolation method alleviates this problem by extrapolating given component map data well below idle speed. The underlying equations of the method are based on the principles of incompressible similarity laws. Also known as pump laws, these equations are modified to account for compressibility effects by varying the similarity law exponents. To estimate the integrity of the extrapolated component maps and to build confidence in the sub-idle extrapolation method, extrapolate speed lines were compared to speed lines found in the original component map. Even though the extrapolation method is yet to be experimentally validated, preliminary estimates showed that the extrapolation method did produce adequate component maps. To demonstrate the potential of the component extrapolation method when used in conjunction with gas turbine performance models, a virtual test case engine was modeled and used to produce start-up performance data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.002 |
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".