A Pareto Multi-Objective Optimization of a Camber Morphing Airfoil using Non-Dominated Sorting Genetic Algorithm
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1583.vid This study uses a multi-objective Non-Dominated Sorting Genetic Algorithm to optimise the aerodynamics of a well-known UAV, the UAS-S45. The optimization algorithm is combined with updated Class Shape Transformation (CST) parameterization to improve aerodynamic performance by increasing lift-to-drag ratio and aerodynamic endurance at various angles of attack. The reference airfoil is parameterized using the CST to give local shape changes and skin flexibility for optimum morphing airfoil combinations. The optimization scheme was carried out with an in-house MATLAB code and this procedure is based on a multi-objective Non-Dominated Sorting Genetic Algorithm coupled to XFoil solver, and validation is done using the SST-K Omega model. The results of the optimizations carried out using different operating conditions are presented; starting from the optimal Pareto fronts, several solutions are selected and compared in terms of airfoil shapes and performance. The results show that the morphing improves the UAS-S45 airfoil's aerodynamic efficiency. The improved airfoils have shown a high improvement in overall aerodynamic performance by up to 65.3% in lift to drag ratio at 8 degrees angle of attack compared to the reference airfoil, and an increase in C_L^(3/2)/C_D of up to 98.8% at 8 degrees for the UAS-S45 optimized airfoil configurations. The optimization increases the overall aerodynamic performance of the configuration and the stall angle from 12º to at least 16º. The method used in this work can be employed as a valuable tool for replacing the traditional slats and flaps at the leading edge and the trailing edge of the airfoil. The pareto optimization analysis will be presented with the optimization results and numerical analysis using high fidelity CFD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".