Generalized Sizing Methodology for Hybrid Aircraft Using Integrated Performance Constraints
Bibliographic record
Abstract
Hybrid aircraft are systems capable of flying as either fixed-wing aircraft or rotorcraft, as well as transition between these modes. Nowadays, hybrid aircraft have many potential applications as they consolidate some of the advantages of fixed-wing aircraft and rotorcraft. Despite the abundant knowledge on designing fixed-wing aircraft, designing hybrid aircraft is a challenging process and considerable design work yet needs to be developed. Diverse hybrid aircraft preliminary design studies have focused on presenting new configurations using traditional design methods for fixed-wing aircraft and/or historical data. Although such approach is practically possible, it may lead to having either insufficient or unneeded excess engine power. Insufficient power prevents aircraft from achieving the desired performance requirements and increases transition flight time. On the other hand, excess power penalizes the design by increasing the aircraft cost and weight, resulting in diverse performance deficiencies. Thus, to obtain adequate power, wing, and rotor disc loadings, this paper presents a formal sizing methodology for hybrid propeller-driven aircraft with the necessary mathematical formulations based on integrating the performance requirements in the fixed-wing, rotorcraft, and transition flight modes. The methodology is verified by estimating the XV-15 tiltrotor sizing parameters with errors less than 3% compared with the real aircraft parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".