Cycle model matching of a regional aircraft engine using high-fidelity flight simulator data
Bibliographic record
Abstract
A thermodynamic cycle model was matched to the CF34-8C5B1 engine using the data of a high-fidelity Level-D flight simulator as a surrogate. Before the model match, the data from the simulator was assessed to determine thermal stability, data reproducibility, and engine-to-engine variation. A series of tests were performed across the flight envelope of the MHI CRJ-700 regional aircraft to match and validate the intended cycle model. A baseline off-design cycle model was established based on an engine design point from previous research. This baseline model allowed the detection of any suspicious data obtained from the flight simulator and made it possible to determine appropriate actions concerning the model match. The baseline thermodynamic model was then adjusted and calibrated to match the data from the simulator at various flight conditions. The cycle model adjustments involved: (1) recalibration of the speed lines of the fan map, and (2) tuning the low-pressure turbine map’s adiabatic efficiency. These variables were selected based on the physics of the problem. Moreover, a simplified matching method was proposed, which allows to optimize the processing time and circumvent convergence problems. The proposed adjustments render a final model that predicts the thrust and engine fuel flow rate of the CF34-8C5B1 engine within ±5.0% relative to the flight simulator engine model for the power settings of interest.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".