Further Exploration of Regional-Class Hybrid Wing-Body Aircraft Through Multifidelity Optimization
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0014.vid Regional-class hybrid wing-body (HWB) aircraft that satisfy various stability and control requirements are optimized for a combination of cruise drag and maximum takeoff weight by using a multifidelity multidisciplinary optimization framework based on solutions to the Reynolds-averaged Navier-Stokes equations. The design mission consists of flying 100 passengers a distance of 2000 NM (3704 km) at an altitude of 36000 ft (10973 m) and a speed of Mach 0.78. HWB aircraft that meet these requirements were previously optimized while ensuring they satisfy static margin and trim requirements at cruise, achieve directional trim with one engine inoperative on the ground using winglet-mounted rudders, and satisfy a rotation constraint using nearly full-span pitch effectors (Reist, T.A., et al., J. of Aircraft, Vol. 56, 2019). However, the rotation constraint is significantly detrimental to performance, and the resulting aircraft are highly unstable at low speeds. The present study addresses the former issue by allowing a lower pitch control margin to saturation and the latter issue by constraining the low-speed trimmed static margin. The geometric flexibility given to the optimizer is then increased. First, the placement and orientation of the polyhedron enclosing the cockpit, cabin, and cargo holds is optimized simultaneously with the geometry while also modeling telescoping nose landing gear to ease the burden of achieving rotation. Second, local shape control is increased through a more refined geometry control system. Through these means and a few other model improvements, an HWB aircraft is found that satisfies the additional low-speed constraints and has a cruise lift-to-drag ratio of 22.8, leading to 15.7% lower cruise drag and 11.2% lower fuel burn for the design mission relative to the previously optimized aircraft.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".