Blended-Wing-Body Regional Aircraft Optimization with High-Fidelity Aerodynamics and Critical Design Requirements
Bibliographic record
Abstract
Conventional tube-and-wing and proposed blended-wing-body airliners must satisfy several design requirements, but the latter configuration is tightly integrated and sensitive to these requirements. In this work, blended-wing-body regional aircraft are investigated using a gradient-based mixed-fidelity multidisciplinary optimization framework centered on a Reynolds-averaged Navier–Stokes solver. In addition to sizing and cruise trim, design requirements considered include one-engine-inoperative directional trim, takeoff rotation ability, takeoff field length, initial climb performance, low-speed trim and static margin, and top-of-climb rate of climb. Results show how optimal design features vary and performance is overpredicted if critical design requirements are excluded and how key elements of geometric freedom help realize the potential of the configuration. A 4.8% block fuel burn benefit is enabled by pivot-piston variable-length landing gear even with low-mounted engines and high design freedom. The off-design constraints penalize block fuel burn by 3.2% if variable-length landing gear is considered, but this value reaches 7.6% if lower geometric freedom that inhibits tight cabin contouring and the formation of a novel forebody ridge is removed. Leading-edge carving is found to be optimal. The high design freedom and high-fidelity aerodynamics model help efficiently satisfy the design requirements, resulting in a cruise lift-to-drag ratio of 21.7 at 36,000 ft and Mach 0.78.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".