Design of Low-Sweep Wings for Maximum Range
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An efficient Newton-Krylov algorithm for high-fidelity aerodynamic shape optimization is used to design low-sweep wings for maximum range at transonic speeds. In this approach, the steady flow solution is obtained using the Newton method with pseudo-transient continuation. The objective function gradient is computed using the discrete-adjoint method. Linear systems from both the flow and adjoint systems are solved using a preconditioned Krylov method. A quasi-Newton optimizer is used to find the search direction. It is coupled with a line-search algorithm. Our single-point optimization results show that it is possible to design shock-free unswept wings at Mach numbers and lift coefficients comparable to the operating conditions of modern transonic transport aircraft. Robust wing designs for low-sweep and unswept wings under the same operating conditions are obtained through multi-point optimization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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