An Engine Deterioration Model for Predicting Fuel Consumption Impact in a Regional Aircraft
Bibliographic record
Abstract
A deterioration cycle model is presented, designed to consider the turbomachinery efficiency losses that are expected during real engine in-service operations. The cycle model was developed using information from practical experience found in the literature to account for both short- and long-term deterioration effects; the former occurring during the first flight cycles, the latter due to regular in-service operation. This paper highlights the importance of analyzing the inter-turbine temperature margin (ITTM) to track engine deterioration to determine the extent of an in-service engine life. The proposed model was used to assess the ITTM and fuel consumption impact in the CRJ-700 regional aircraft (powered by two CF34-8C5B1 engines) for three representative missions: short (0.4 h), average (1.4 h), and long (2.5 h), considering different levels of engine deterioration, from the new engine level up to fully deteriorated. The fuel consumption at the new engine level (zero deterioration) was validated against a real-time engine model embedded in a Level-D flight simulator, the so-called Virtual Research Flight Simulator located at the Laboratory of Applied Research in Active Control, Avionics, and AeroServoElasticity. The errors found in this validation for the trip mission fuel consumption in the short, average, and long missions were −3.6, +0.9, and +0.6%, respectively. The cycle model predictions suggest the ITTM for a new engine is 55.2 °C, whereas for a fully deteriorated engine, it is 26.4 °C. Finally, in terms of fuel consumption, the results presented here show that an average CF34-8C5B1 engine shows an increase in the cumulative fuel consumption of 2.25% during its life in service, which translates to a 4.5% impact in aircraft fuel consumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".