New Generic Turbofan Model for High-Fidelity Off-Design Studies
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3642.vid In this paper, a high-fidelity aerothermodynamic Off-Design Generic Model is proposed. The model was completely developed in-house at the Laboratory of Applied Research in Active Control, Avionics and AeroServoElasticity using Matlab. The Design Point and the turbomachinery Component Maps scaling factors are proposed and discussed. Additionally, the set of nonlinear equations that define the Off-Design model are established, furthermore, two numerical methods to solve the system of equations are briefly reviewed. The Off-Design Generic Model results are compared against those of the Numerical Propulsion System Simulation, a high-fidelity platform for aerothermodynamic simulations used in the Gas Turbine Engine industry. A series of considerations are proposed to prevent any systematic bias in the comparison between the two models. The Generic Model proposed in this work presented good precision compared to the Numerical Propulsion System Simulation. From representative conditions (Sea-Level, 20k, and 35k) at different power settings, the average errors found in the Specific Fuel Consumption are negligible (less than ± 0.06%), and these errors in the net thrust were +0.03%, +0.25%, and +0.29%, 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".