High-Fidelity Aerodynamic Shape Optimization of a Lifting-Fuselage Concept for Regional Aircraft
Bibliographic record
Abstract
High-fidelity aerodynamic shape optimization based on the Reynolds-averaged Navier–Stokes equations is used to optimize the aerodynamic performance of a conventional tube-and-wing design, a hybrid wing-body, and a novel lifting-fuselage concept for regional-class aircraft. Trim-constrained drag minimization is performed on a hybrid wing-body design, with an optimized conventional design serving as a performance reference. The optimized regional-class hybrid wing-body yields no drag savings when compared with the conventional reference aircraft. Starting from the optimized hybrid wing-body, an exploratory optimization with significant geometric freedom is then performed, resulting in a novel shape with a slender lifting fuselage and distinct wings. Based on this exploratory result, a new regional-class lifting-fuselage configuration is designed and optimized. With a span constrained by code “C” gate limits and having the same wing-only span as the conventional reference aircraft, this new design produces up to 10% lower drag than the reference aircraft. The effect of structural weight uncertainties, cruise altitude, and stability requirements are also examined.
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 | 0.000 | 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".