Optimization of flatback airfoils for wind turbine blades using a genetic algorithm with an artificial neural network

Xiaomin Chen, Ramesh Agarwal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In this paper, we employ a genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA is significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT is used for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. It is shown that the combined GA/ANN optimization technique is capable of accurately and efficiently finding globally optimal flatback airfoils.

Original languageEnglish
Title of host publication48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781600867392
DOIs
StatePublished - 2010

Publication series

Name48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

Fingerprint

Dive into the research topics of 'Optimization of flatback airfoils for wind turbine blades using a genetic algorithm with an artificial neural network'. Together they form a unique fingerprint.

Cite this