Thrust Rebalance to Extend Engine Time On-Wing With Consideration of Engine Degradation and Creep Life Consumption
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 is crucial to lowering the costs. The engine time on-wing is often limited by the expiration of Life Limiting Parts, performance deterioration, maintenance schedule, etc. This paper presents a novel method of rebalancing the thrust of engines of an aircraft to maximize the time between overhauls 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 GT90 115B to test the feasibility of the method with one engine degraded and the other engine undegraded. The obtained results demonstrate that for the aircraft flying between London and Toronto with 5,000 nominal flight cycles given to the engines, the time on-wing of the degraded engine could drop from 5,000 to 2,460 flight days due to its HP turbine degradation (1% efficiency degradation 3% flow capacity degradation), causing the same level of drop of time between overhauls of the aircraft. The time on-wing of the degraded engine could increase from 2,460 flight days without thrust rebalance to 3,410 flight days with thrust rebalance, i. e. around 38.6% potential improvement for the time between overhauls 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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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 | 0.000 |
| 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) | 0.001 | 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".