Progress in the Application of an Aerodynamic Shape Optimization Capability Using Hybrid Laminar Flow Control to Airfoils and Infinite Swept Wings
Bibliographic record
Abstract
The use of hybrid laminar flow control can extend the region of laminar flow on a wing at sweep angles and Reynolds numbers beyond those for which natural laminar flow is effective. In this paper, a suction boundary condition implemented in a Reynolds-averaged Navier-Stokes aerodynamic shape optimization framework coupled with the SA-sLM2015cc local correlation-based transition model is applied to lift-constrained drag-minimization of airfoils and wings. The transition location was determined for a supercritical airfoil, after which suction was applied upstream of this location. After determining the resulting transition location on the upper surface, suction is applied upstream of the new location while retaining the original suction location. By adding a second suction location at this new transition location, further drag reductions were obtained, indicating the successful application of multiple suction locations at varying suction velocities. The airfoil is then optimized with suction applied upstream of the baseline transition location, yielding a higher drag reduction compared to the optimization without the application of suction. Additionally, the airfoil was optimized without suction and the suction boundary condition was then applied upstream of the new transition location. These results indicated that the application of suction to an optimized geometry yields a higher drag reduction compared to the case where suction is applied to the baseline geometry, which is then optimized. To investigate the effect of suction on crossflow instabilities, an infinite swept wing is tested with suction added upstream at a single location on the upper surface, lower surface and both surfaces. This geometry was optimized with and without suction, and the results with suction yielded a higher drag reduction compared with those without. In contrast to the airfoil case without sweep, on the infinite swept wing when suction is applied to a geometry optimized without suction, the drag reduction is lower. These results demonstrate that the presented approach to modelling suction and transition provides a promising methodology to study and optimize wings for hybrid laminar flow control.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".