Further Exploration of Regional-Class Hybrid Wing-Body Aircraft Through Multifidelity Optimization
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it