Aircraft Mathematical Model Identification for Flight Trajectories and Performance Analysis in Cruise
Bibliographic record
Abstract
In this paper, a mathematical model for estimating the performance and flight trajectories in cruise is identified from data available in flight manuals. The first part of this paper focuses on the design of a fuel flow and emissions model. Starting from the equations of motion of an aircraft in cruise, a simplified model representing the fuel flow in a corrected form was developed. A practical algorithm was next developed to identify the aircraft model parameters and to determine the mathematical structure that reflects its fuel flow. This process was done using performance data available in the aircraft flight crew operating manual. The emissions model was also developed based on data available in the International Civil Aviation Organization’s engine emissions databank. The second part of the paper deals with the development of algorithms for predicting the trajectories and calculating the optimal speeds (i.e., maximum range, long range, and economy) of the aircraft in cruise. Practical techniques for storing and retrieving information without using optimization algorithms have been considered. The methodology was applied on both a Cessna Citation X business jet and Bombardier CRJ-700 regional jet aircraft. The comparison results showed a very good agreement for the fuel consumption and optimal speed.
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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 | 0.002 |
| 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 | 0.001 |
| 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".