Design and performances of a Turbo-Electric and Distributed Propulsion aircraft in the Small and Medium Range segment: intermediate results from the European project IMOTHEP
Bibliographic record
Abstract
This paper presents the intermediate results of the European project IMOTHEP: "Inves- tigation and Maturation of Technologies for Hybrid Electric Propulsion". The IMOTHEP project aims at improving the evaluation of the potential of hybrid electric propulsion to reduce fuel consumption for both regional aircraft and Small and Medium Range (SMR) aircraft. This publication introduces the work performed on the SMR conservative (or SMR-CON) aircraft during the intermediate design loop. The SMR-CON is a tube and wing aircraft equipped with a turbo-electric architecture and Distributed Electric Propulsion. The intermediate design loop consisted in designing propulsive components based on the requirement derived from the initial design loop and integrating them in the overall aircraft design tool for the evaluation of performance and design sensitivities. Compared to the initial design loop, it was found that the first component designs reached a level of performance insufficient to show a benefit of a turbo-electric aircraft over a traditional UHBR turbofan aircraft in the SMR segment. Sensitivity analysis and decomposition of efficiency of the turbo-electric chain identifies possible ways of improvements among which the efficiency of the turbomachine is a key parameter.
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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 | 0.001 |
| 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 | 0.001 | 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".