Aerodynamic Optimization of a Transonic Strut-Braced-Wing Regional Aircraft Based on the Reynolds-Averaged Navier-Stokes Equations
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2526.vid The aerodynamic design and fuel burn performance of a Mach 0.78 strut-braced-wing regional jet is investigated through aerodynamic shape optimization based on the Reynolds-averaged Navier-Stokes equations. Conceptual-level multidisciplinary design optimization is first performed to size the strut-braced-wing aircraft for a design mission similar to the Embraer E190-E2, with a design range of 3,100 nmi at a maximum capacity of 104 passengers, and a maximum payload of 30,200 lb. For direct performance comparisons, a conventional tube-and-wing regional jet is also sized and optimized based on the same reference aircraft. Gradient-based aerodynamic shape optimization is then performed on wing-body-tail models of each aircraft, with the objective of drag minimization at cruise over a 500 nmi nominal mission. Design variables include twist and section shape degrees of freedom, which are realized through a free-form and axial deformation geometry control system, while nonlinear constraints include constant lift, zero pitching moment, minimum wing volume, and minimum maximum thickness-to-chord ratios. Results indicate that the optimizer is capable of mitigating shock formation, boundary-layer separation, and other flow interference effects from each wing design, including those within the wing-strut junction of the strut-braced wing. With current technology levels, the strut-braced-wing regional jet offers a 15.6% improvement in cruise lift-to-drag ratio over the equivalent conventional tube-and-wing aircraft, which translates to a 6.5% reduction in block fuel for the nominal mission.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".