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.
Skip Nav Destination
ASME 2010 4th International Conference on Energy Sustainability
May 17–22, 2010
Phoenix, Arizona, USA
Conference Sponsors:
- Advanced Energy Systems Division and Solar Energy Division
ISBN:
978-0-7918-4395-6
PROCEEDINGS PAPER
Optimization of Flatback Airfoils for Wind Turbine Blades
Xiaomin Chen,
Xiaomin Chen
Washington University in St. Louis, St. Louis, MO
Search for other works by this author on:
Ramesh Agarwal
Ramesh Agarwal
Washington University in St. Louis, St. Louis, MO
Search for other works by this author on:
Xiaomin Chen
Washington University in St. Louis, St. Louis, MO
Ramesh Agarwal
Washington University in St. Louis, St. Louis, MO
Paper No:
ES2010-90373, pp. 845-853; 9 pages
Published Online:
December 22, 2010
Citation
Chen, X, & Agarwal, R. "Optimization of Flatback Airfoils for Wind Turbine Blades." Proceedings of the ASME 2010 4th International Conference on Energy Sustainability. ASME 2010 4th International Conference on Energy Sustainability, Volume 2. Phoenix, Arizona, USA. May 17–22, 2010. pp. 845-853. ASME. https://doi.org/10.1115/ES2010-90373
Download citation file:
5
Views
0
Citations
Related Proceedings Papers
Related Articles
Shape Optimization of a Multi-Element Foil Using an Evolutionary Algorithm
J. Fluids Eng (May,2010)
A Correlation-Based Transition Model Using Local Variables—Part II:
Test Cases and Industrial Applications
J. Turbomach (January,0001)
A Parallelized Coupled Navier-Stokes/Vortex-Panel Solver
J. Sol. Energy Eng (November,2005)
Related Chapters
Forecasting for Reservoir's Water Flow Dispatching Based on RBF Neural Network Optimized by Genetic Algorithm
International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)
Feature Selection of Microarray Data Using Genetic Algorithms and Artificial Neural Networks
Intelligent Engineering Systems through Artificial Neural Networks
Stock Market Trend Prediction Using Levenberg-Marquardt Neural Network Optimized by Genetic Algorithm
International Conference on Software Technology and Engineering (ICSTE 2012)