CFD Based Wing Shape Optimization Through Gradient-Based Method
Bibliographic record
Abstract
a low-cost alternative to evaluate its performance. This paper presents and discusses a method for the numerical evaluation of a given shape and its possible optimization regarding its aerodynamic performance. The computational domain is obtained by means of a b-spline curve shape parametrization, the control points are described in an input le and 2D geometry is constructed with a 2D mesher. A valid CFD domain is obtained from constructing a 3D geometry from the 2D information, additional parameters and a volumetric mesher. The aerodynamic information is obtained by solving the Navier-Stokes equations using the OpenFOAM (Opensource Field Operation And Manipulation) toolkit. The method is particularly useful to narrow a design search space for an aerodynamic shape, in which case the proof of concept that this paper presents is an airfoil. This model could be used for initial approximations to improve aerodynamic behavior of a given shape. CFD simulation could deliver accurate predictions of how a given shape would perform under various boundary conditions, characteristic that makes this method more attractive. This type of multidisciplinary design optimizations have been implemented lately and can be implemented to various industrial applications as seen in [5] and [6].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".