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Airfoil Topology Optimization using Teaching-Learning based Optimization

机译：使用基于学习的优化的机翼拓扑优化

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摘要

This paper primarily deals with the optimization of airfoil topology using teaching-learning based optimization, a recently proposed heuristic technique, investigating performance in comparison to Genetic Algorithm and Particle Swarm Optimization. Airfoil parametrization and co-ordinate manipulations are accomplished using piecewise b-spline curves using thickness and camber for constraining the design space. The aimed objective of the exercise was easy computation, and incorporation of the scheme into the conceptual design phase of a low-reynolds number UAV for the SAEA erodesign Competition. The 2D aerodynamic analyses and optimization routine are accomplished using the Xfoil code and MATLAB respectively. The effects of changing the number of design variables is presented. Also, the investigation shows better performance in the case of Teaching-Learning based optimization and Particle swarm optimization in comparison to Genetic Algorithm.
机译：本文主要通过使用基于教学的优化（一种最近提出的启发式技术）来研究机翼拓扑的优化，与遗传算法和粒子群优化相比，研究性能。翼型参数化和坐标操纵是使用分段b样条曲线完成的，该曲线使用厚度和曲面来约束设计空间。该演习的目标是易于计算，并将该方案纳入SAEA航空设计竞赛的低雷诺数无人机的概念设计阶段。分别使用Xfoil代码和MATLAB完成2D空气动力学分析和优化例程。提出了更改设计变量数量的影响。此外，与基于遗传算法的研究相比，在基于教学学习的优化和粒子群优化的情况下，调查显示了更好的性能。

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