Modeling aircraft jet engine and system identification by using Genetic Programming
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new approach for discovering an aircraft jet engine model is proposed by using system identification and Genetic Programming (GP). The relationship between the engine Exhaust Gas Temperature (EGT), as a major indicator of the engine health condition, and other engine parameters and operating conditions corresponding to different phases of the flight is modelled by using GP technique. Toward this end, flight characteristics are divided into several phases such as the take off and the cruise. The GP scheme is then used to discover the structure of the interrelations among engine parameters. This approach provides an effective strategy to estimate the aircraft jet engine EGT without requiring any specific information on the internal engine model and characteristics. The performance of the proposed algorithm is demonstrated by applying it to a dual spool engine data that is generated by using the Gas turbine Simulation Program (GSP) software.
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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