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Optimum NURBS Curve Fitting for Geometry Parameterization of Gas Turbine Blades’ Sections: Part II — Swarm Intelligence Techniques

[+] Author Affiliations
A. Safari, H. G. Lemu

University of Stavanger, Stavanger, Norway

Paper No. IMECE2012-89120, pp. 67-72; 6 pages
doi:10.1115/IMECE2012-89120
From:
  • ASME 2012 International Mechanical Engineering Congress and Exposition
  • Volume 1: Advances in Aerospace Technology
  • Houston, Texas, USA, November 9–15, 2012
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4517-2
  • Copyright © 2012 by ASME

abstract

In part I of this study an optimum NURBS curve fitting by two evolutionary optimization techniques was successfully designed. These methods were implemented to optimize the location of a set of NURBS control points for the measured point cloud of four segments of a gas turbine compressor airfoil shape. The purpose of the optimization was to demonstrate the good ability of evolutionary techniques, in particular Genetic Algorithms, in optimizing such curve fitting problems. The objective of part II is to examine two alternative solutions for NURBS curve fitting of the same airfoil point cloud with swarm intelligence optimization technique. Indeed, the same work has been done by applying two basically different optimization approaches that is Particle Swarm Optimization and Invasive Weed Optimization. Results allow seeing a number of advantages as well as some disadvantages in this optimum curve fitting approach in comparison to the previous techniques applied by authors.

Copyright © 2012 by ASME

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