Application of Aerodynamic Shape Optimization to Swept Natural Laminar Flow Wings
Bibliographic record
Abstract
The application of natural laminar flow has been limited to modest Reynolds numbers and sweep angles due to the effect of crossflow instabilities on boundary-layer transition for transonic swept-wing aircraft. In this paper a Reynolds-averaged Navier-Stokes based aerodynamic shape optimization framework coupled with the SA-sLM2015cc local correlation-based transition model is applied to lift-constrained drag minimization of infinite swept wings at the cruise conditions of business and regional jets and of a finite swept wing at the cruise conditions of a single-aisle aircraft. In the case of infinite swept wings, the optimizations achieve a profile drag reduction of 20% for the regional jet and 40% for the business jet, corresponding to estimated aircraft-level drag reductions of 5% and 10%, respectively, compared to an infinite swept wing optimized and analyzed under fully-turbulent conditions. This reduction is attributed to a combination of decreases in pressure drag and viscous drag resulting from the region of laminar flow on both surfaces. Furthermore, a multipoint optimization demonstrates the robustness of the optimized design to changes in Mach number and coefficient of lift. For the finite-wing optimization, there is a wing drag reduction of 21% compared to the baseline configuration and 10% compared to a wing optimized and analyzed under fully-turbulent conditions. The latter corresponds to an estimated aircraft-level drag reduction of 5%. Finally, the design space associated with lift-constrained drag minimization of a low-speed airfoil is studied with respect to multimodality. Two clearly distinct local minima are found with substantially different geometries and performance, indicating that multimodality may be a more significant issue when optimizing with laminar-turbulent transition prediction.
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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 | 0.001 |
| 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 | 0.001 |
| 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".