Cessna Citation X Engine Model Identification from Flight Tests
Bibliographic record
Abstract
During aircraft development, mathematical models are elaborated from our knowledge of fundamental physical laws. Those models are used to gain knowledge in order to make decisions in all development stages. Since engine model is one of the most important items in aircraft simulation, the aviation industry has recently developed a high interest on them. With the power capacities development in the last years, numerical simulations have been widely used for predicting engine response. In this paper, a methodology to identify an engine model from flight tests is presented. A Cessna Citation X Level D Flight Simulator designed and manufactured by CAE Inc. was used to sample the engine thrust force data. More than 500 flight tests were made for different flight conditions expressed in Mach numbers (M = 0 to M = 0.9), altitudes (h = 0 ft to h = 50,000 ft) and different throttle positions (idle to maximum). The engine mathematical model was developed from analysis and comparison of several existing engine models in the literature. Two estimation algorithms were created to identify the parameters defining the model. To validate the model, simulations were performed and compared. Results have shown that the obtained model was accurate and could be further used to estimate the engine thrust forces for any flight conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".