Airfoil Topology Optimization using Teaching-Learning based Optimization

Airfoil Topology Optimization using Teaching-Learning based Optimization

Dushhyanth Rajaram, Himanshu Akhria, S. N. Omkar
Copyright: © 2015 |Volume: 6 |Issue: 1 |Pages: 12
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466677913|DOI: 10.4018/ijamc.2015010102
Cite Article Cite Article

MLA

Rajaram, Dushhyanth, et al. "Airfoil Topology Optimization using Teaching-Learning based Optimization." IJAMC vol.6, no.1 2015: pp.23-34. http://doi.org/10.4018/ijamc.2015010102

APA

Rajaram, D., Akhria, H., & Omkar, S. N. (2015). Airfoil Topology Optimization using Teaching-Learning based Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 6(1), 23-34. http://doi.org/10.4018/ijamc.2015010102

Chicago

Rajaram, Dushhyanth, Himanshu Akhria, and S. N. Omkar. "Airfoil Topology Optimization using Teaching-Learning based Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 6, no.1: 23-34. http://doi.org/10.4018/ijamc.2015010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

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 SAE Aerodesign 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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.