A study of the viscous optimization of the shape of a non-lifting strut
Bibliographic record
Abstract
The objective of this work is to gain insight into the process and development of a method for obtaining optimum design shapes for non-lifting aerodynamic struts while employing an interactive viscouspotential flow model for a range of airfoil reynolds numbers. This was done for axially loaded struts with constant cross-sectional area as well as struts loaded in bending with a fixed cross-sectional moment of inertia. The optimization sought the airfoil shape that resulted in minimum drag. The flow field was obtained by using a panel method that was iteratively coupled to a boundary layer solver. The viscous solver used was to model the boundary layer and was based on the zero-equation, Cebeci-Smith turbulence model. The main flow field was computed using a panel method. The airfoil shape was described using a bezier-PArSEC shape parameterization and optimization of the shape parameters was obtained using differential evolution. The numerical approach of the flow field solver and the simplicity of the genetic algorithm allowed for these results to be obtained in an acceptable timely manner. This paper will present the results of a number of cases and discuss all of the issues that arose. While one can have confidence in the results, limitations and the need for future work were also exposed. The limitations occurred in this thesis were due to the limitations of the boundary layer flow field solver. This solver did not allow airfoils with significant thickness to be evaluated thus restricting the solution space to thin airfoils. It was observed that future work on dealing with separation modelling needs to be done to allow improved certainty of the optimization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".