Statistical Approach for Electric Taxiing Requirements for Regional Turboprop Aircraft
Bibliographic record
Abstract
Electric motorization of landing gear appears to be one of the alternative solutions to reduce fuel burn, carbon dioxide emissions, and noise during the taxi phase. Because turboprop aircraft operate on short routes, the taxi phase represents an important part of both flight time and fuel consumption. An electric taxiing system (ETS) sized to meet current operational practices could reduce the fuel consumption and remain nearly transparent to the pilots. This paper first presents a statistical approach to define the taxiing requirements for regional turboprop aircraft using 200 taxi phases of 77 aircraft. Requirements of [Formula: see text] maximum acceleration until 15 kt, a 25 kt top speed, and a 13,000 ft distance (including taxi-in and taxi-out) are determined in accordance with the analysis, operational practices, and pilots’ routines. For a speed higher than 15 kt, the acceleration requirement is adjusted using the isopower to limit the mass of the ETS. Then, an ETS with sufficient performance is sized to be integrated in the main landing gear of a regional turboprop aircraft (Dash 8-300). For a standard mission of 270 nautical miles, the expected fuel economy is 3.1% for a payload loss of 2.2% or 1.3 passengers due to the system weight.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".