Toward High-Fidelity Aerodynamic Shape Optimization for Natural Laminar Flow
Why this work is in the frame
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Bibliographic record
Abstract
High-fidelity aerodynamic shape optimization frameworks capable of efficiently incorporating and exploiting laminar-turbulent transition enable the design of aircraft with significantly reduced drag. This work presents recent progress toward that end. First, a two-dimensional Reynolds-averaged Navier-Stokes (RANS) flow solver is extended to incorporate an iterative laminar-turbulent transition prediction methodology. The natural transition locations due to Tollmien-Schlichting instabilities are predicted using the compressible form of the Arnal-Habiballah-Delcourt criterion or alternatively, the simplified e envelope method of Drela and Giles. The boundary-layer properties are obtained directly from the Navier-Stokes flow solution and the transition to turbulent flow is modeled using an intermittency function. The RANS solver is subsequently employed in a gradient-based sequential quadratic programming shape optimization framework. The laminar-turbulent transition criteria are tightly coupled into the objective and gradient evaluations. The gradients are obtained using a parallelized finite-difference approximation. The proposed optimization framework is applied to the lift-constrained drag minimization of airfoils at various flight conditions, leading to natural laminar flow designs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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