Direct Search Airfoil Optimization Using Far-Field Drag Decomposition Results
Bibliographic record
Abstract
For this research project, two airfoils have been optimized using a Direct Search optimization algorithm and a cost function determined from the results of a fareld drag decomposition method. The latter is a powerful tool allowing to breakdown the drag into wave, viscous, induced and spurious drags. The latter type of drag is caused by numerical and truncation errors, as well as by the addition of arti cial viscosity by most solvers to smooth strong gradients. Furthermore, the spurious drag is dependent on the con guration: a blunt body will produce more spurious drag than a slender body. Thus, if an optimization process is based on the total drag it will tend to nd a con guration that reduces among others, the spurious drag which can limit its e ciency. The optimization process in this research used the net drag only, excluding the spurious drag. First, the NACA0012 airfoil in an Euler ow at Ma = 0.85 was optimized. The nal conguration had a at nose shape and an almost constant thickness along the chord. The computed net drag was 74 d.c., an improvement of 393 d.c. An additional control optimization was done, but on the total drag. The optimized con guration was more rounded, which is a direct consequence of including the spurious drag in the objective function. This shows that the spurious drag has a large in uence on the optimum airfoil. Second, the RAE2822 airfoil in viscous ow with a constant lift coe cient of 0.824 and a Mach number of 0.734 was optimized. The nal con guration was thinner than the original airfoil on the rst 50% of the chord length, then got thicker for the rest of the chord length. The con guration also showed a cambered trailing edge typical of supercritical airfoils. The computed net drag value was 104.3 d.c., an improvement of 83 d.c. Most of the improvement had been achieved by the wave drag reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".