Identification and Validation of an Engine Performance Database Model for the Flight Management System
Bibliographic record
Abstract
This Paper presents the validation studies results of an engine mathematical performance model identification for flight management system trajectory prediction and optimization applications. The methodology was applied to the Cessna Citation X business aircraft, for which the aircraft flight manual and the flight crew operating manual are available. In addition, another data source based on computerized trajectory was also used to generate several climb and descent flight profiles required in the engine model identification process. To demonstrate and further validate the accuracy of the proposed engine performance model, a level-D research aircraft flight simulator of the Cessna Citation X was used as a reference. According to the Federal Aviation Administration (FAA, AC 120-40B), level D corresponds to the highest qualification level for the flight dynamics and engine modeling. Validation of the methodology was accomplished by comparing the prediction model with a series of flight data collected with the flight simulator for different flight conditions and different flight phases including takeoff, climb, cruise, and idle descent. Comparison results were validated with a tolerance of for each engine performance predicted by the model in terms of fan speed, core speed, thrust, and fuel flow.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".