Top Level Aircraft Requirements relaxation for a single-aisle aircraft: a case study on fleet-wide CO2 emissions and economic impacts
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1357.vid Reducing aviation climate impact is a priority for the stakeholders of the sector, but doing so requires increased investments and progress in aircraft technology, low-carbon fuels, and operations. Aircraft are generally very versatile, leading to missions that are far from the ones they have been designed and optimized for. A potential lever for CO2 reduction in aviation is to have an overall fleet that is better tailored and optimized for the needs of the air transport system. Aircraft are generally designed with a set of top-level aircraft requirements to ensure they match operational constraints. This paper proposes to relax these requirements to assess the effects of lower-range, slower and modified payload aircraft, and evaluate the potential gains for the air transport system. Per-passenger and kilometer fuel burn and operating costs of several modified aircraft are compared with a baseline aircraft using a payload-range response surface. It allows a quick estimation of any mission fuel burn and operating cost. United States-wide operations emissions abatement potential is assessed by replacing the reference fleet with the newly designed aircraft when it is more efficient. Marginal fuel burn gains of 6 % are found when a shorter-range aircraft is designed. An illustrative slower open-rotor aircraft would not significantly increase its operating costs. Increasing the capacity offers both cost and fuel burn reductions but decreases the operating versatility.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".