Aircraft Engine Performance Model Identification using Artificial Neural Networks
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-3247.vid This paper presents a methodology developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticiy (LARCASE) to identify a performance model of the engine powering the CRJ-700 regional jet aircraft from flight data using neural networks. To this end, a qualified virtual research simulator (VRESIM) was used to conduct several categories of flight tests and collect engine data under a wide range of operating conditions. The collected data were then used to create a comprehensive database for the training process. This process was performed using the Bayesian regularization algorithm available in the Matlab Neural Networks Toobox, and a study was carried out to estimate the optimal number of neurons in the network structure. 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 descent. The results showed that the model was able to predict the engine performance in terms of fan speed, thrust and fuel flow with very good accuracy.
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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) | 0.000 | 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 | 0.000 |
| 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".