Improved Local Scale Generic Cycle Model for Aerothermodynamic Simulations of Gas Turbine Engines for Propulsion
Bibliographic record
Abstract
A local scale Aerothermodynamic Generic Cycle Model (AGCM) is proposed. The AGCM accounts for several improvements not considered in similar models, such as compressor bleed extraction for aircraft Environmental Control System (ECS), parasitic shaft power extraction, and the enthalpy of the fuel entering the combustor. The AGCM is intended for steady-state Design Point (DP) and Off-Design (OD) performance analyses. The underlying physics is presented for the DP model. The turbomachinery component maps scaling and the system of nonlinear equations necessary to define the OD model are thoroughly discussed. The AGCM is compared with an equivalent model developed in the Numerical Propulsion System Simulation (NPSS). The comparisons were performed considering a DP envisioned to approximate a General Electric CF34-8C5B1 engine. The average errors found in these comparisons for the Specific Fuel Consumption (SFC) and net thrust were −0.111% and 0.193%, respectively. Finally, the predictions of the absolute levels of performance intended for the -8C5B1 engine are compared with empirical correlations derived from a comprehensive turbofan engine database. It was found that the predictions of the AGCM are in agreement with the empirical correlations; the errors found in SFC and net thrust at cruise flight condition were −0.43% and 2.06%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".