Real-Time Execution of a High Fidelity Aero-Thermodynamic Turbofan Engine Simulation
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".