Development of an Adaptive Aero-Propulsive Performance Model in Cruise Flight – Application to the Cessna Citation X
Why this work is in the frame
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Bibliographic record
Abstract
To accurately predict the amount of fuel needed by an aircraft for a given flight, a performance model must account for engine and airframe degradation. This paper presents a methodology to identify an aero-propulsive model to predict the fuel flow of an aircraft in cruise, while considering initial modeling uncertainties and performance variation over time due to degradation. Starting from performance data obtained from a Research Aircraft Flight Simulator, an initial aero-propulsive model was identified using different estimation methods. The estimation methods studied in this paper were polynomial interpolation, thin-plate splines, and neural networks. The aero-propulsive model was then structured using two lookup tables: one lookup table reflecting the aerodynamic performance, and another table reflecting the propulsive performance. Subsequently, an adaptative technique was developed to locally and then globally, adapt the lookup tables defining the aero-propulsive model using flight data. The methodology was applied to the Cessna Citation X business jet aircraft, for which a highly qualified level D research aircraft flight simulator was available. The results demonstrated that by using the proposed aero-propulsive performance model, it was possible to predict the aerodynamic performance with an average relative error of 0.99%, and the propulsive performance with an average relative error of 3.38%. These results were obtained using the neural network estimation method.
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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