CESSNA Citation X Engine Model Identification using Neural Networks and Extended Great Deluge Algorithms
Bibliographic record
Abstract
Accurate numerical engine model is an enabling factor in aircraft performance evaluation and improvement.In this work, nonlinear engine input-output relationships are learned and predicted by two cascading multilayer feedforward neural networks.Machine learning approaches necessitate a great amount of data to achieve efficiency.To satisfy this operational requirement, 441,000 flight cases are designed for a Cessna Citation X turbofan engine using a Level D Research Aircraft Flight Simulator designed and manufactured by CAE Inc.For each flight case, cruise phase data comprising Mach number, altitude, throttle level angle, low-pressure compressor speed, high-pressure compressor speed, engine net thrust and engine fuel flow are recorded.These data are then organized into subsets for training and validation purposes.Each neural network configuration is obtained by means of the Extended Great Deluge algorithm.The latter is also responsible for coordinating neural network training and learning error computation.Analyses based on computer experiments showed a mean relative prediction error upper bound of 4% is achievable for engine output parameters for all cruise phase flight cases.
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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) | 0.000 | 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) | 0.001 | 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 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".