Thrust Rebalance to Extend Engine Time On-Wing With Consideration of Engine Degradation and Creep Life Consumption
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Over the years, airlines have consistently attempted to lower their operational costs and improve aircraft availability by applying various technologies. Engine maintenance expenses are one of the most substantial costs for aircraft operations, accounting for around 30% of overall aircraft operational costs. So, maximizing aircraft time between overhaul (TBO) is crucial to lowering the costs. The engine time on-wing is often limited due to the expiration of Life Limiting Parts, performance deterioration, etc. This paper presents a novel method of rebalancing the thrust of engines of an aircraft to maximize the time between overhaul of the aircraft considering the performance degradation and creep life consumption of the engines. The method is applied to a model aircraft fitted with two model engines similar to GE90-115B to test the feasibility of the method with one engine degraded and the other engine undergraded. The obtained results demonstrate that for the aircraft flying between London and Toronto with 5000 nominal flight cycles given to the engines, the time on-wing of the degraded engine could drop from 5000 to 2460 flight days due to its high-pressure (HP) turbine degradation (1% efficiency degradation and 3% flow capacity degradation), causing the same level of drop of time between overhaul of the aircraft. The time on-wing of the degraded engine could increase from 2460 flight days without thrust rebalance to 3410 flight days with thrust rebalance, i. e. around 38.6% potential improvement for the time between overhaul of the aircraft at the expenses of increased creep life consumption rate of the clean engine. The proposed method could be applied to other aircraft and engines.
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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