Aerodynamic Optimization and Fuel Burn Evaluation of a Transonic Strut-Braced-Wing Single-Aisle Aircraft
Bibliographic record
Abstract
This paper presents an assessment of the potential fuel burn savings offered by the transonic strut-braced-wing configuration within the single-aisle class of aircraft relative to a modern conventional tube-and-wing aircraft through aerodynamic shape optimization based on the Reynolds-averaged Navier–Stokes equations. A representative strut-braced-wing aircraft is first developed through conceptual multidisciplinary design optimization based on the Airbus A320neo, with current technology levels assumed. A concept for the conventional tube-and-wing configuration is also developed to represent the Airbus A320neo as a performance baseline. Single-point aerodynamic shape optimization is then performed on wing–body–tail models of each aircraft to address aerodynamic design challenges and to provide more accurate performance estimates. Results indicate that shock formation can be mitigated from the wing–strut junction of the strut-braced wing at Mach 0.78 and a relatively high design lift coefficient of 0.750, providing an 8.2% reduction in block fuel over a 1000 n mile nominal mission when compared to the conventional tube-and-wing aircraft. Multipoint aerodynamic shape optimization is then performed to build toward a more credible estimate of fuel burn performance, with results showing a reduction in the fuel burn savings to 7.8% at the nominal design point relative to the conventional tube-and-wing aircraft to maintain a 7.6–8.0% improvement over the envelope of operating conditions, which includes design points at even higher Mach numbers and lift coefficients. These results demonstrate the viability of the transonic strut-braced-wing configuration for transport aircraft within the single-aisle class and its potential for reducing commercial fleet fuel burn.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".