Real-Time Execution of a High Fidelity Aero-Thermodynamic Turbofan Engine Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past, a typical way of executing simulations in a real-time environment had been to use transfer function models, state-variable models, or reduced-order aero-thermodynamic models. These models are typically not as accurate as the conventional full-fidelity aero-thermodynamic simulations used as a basis for the generation of real-time models. Also, there is a cost associated with the creation and maintenance of these derived real-time models. The ultimate goal is to use the high fidelity aero-thermodynamic simulation as the real-time model. However, execution of the high fidelity aero-thermodynamic simulation in a real-time environment is a challenging objective since accuracy of the simulation cannot be sacrificed to optimize execution speed, yet execution speed still has to be limited by some means to fit into real-time constraint. This paper discusses the methodology used to resolve this challenge, thereby enabling use of a contemporary turbofan engine high fidelity aero-thermodynamic simulation in real-time environments. This publication reflects the work that was initially presented at the ASME Turbo Expo 2011 (Fuksman and Sirica, 2011, “Real-Time Execution of a High Fidelity Aero-Thermodynamic Turbofan Engine Simulation,” ASME Turbo Expo, Jun. 6-10, Vancouver, Canada, Paper No. GT2011-46661).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it