Low Reynolds number airfoil optimization for wind turbine applications using genetic algorithm

2013 ◽  
Vol 5 (5) ◽  
pp. 052007 ◽  
Author(s):  
Krishnil R. Ram ◽  
Sunil Lal ◽  
M. Rafiuddin Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Reynolds Number ◽  
Wind Turbine ◽  
Low Reynolds Number ◽  
Airfoil Optimization ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

scholarly journals Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Aerospace ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 35
Author(s):  
Abu Bakar ◽  
Ke Li ◽  
Haobo Liu ◽  
Ziqi Xu ◽  
Marco Alessandrini ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reynolds Number ◽  
Convolutional Neural Network ◽  
Low Reynolds Number ◽  
Multi Objective Optimization ◽  
Aerodynamic Coefficients ◽  
Nsga Ii ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.

Studies on a low Reynolds number airfoil for small wind turbine applications

2011 ◽  
Vol 54 (7) ◽  
pp. 1684-1688 ◽  
Author(s):  
Joji Wata ◽  
Mohammed Faizal ◽  
Boniface Talu ◽  
Lesia Vanawalu ◽  
Puamau Sotia ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Wind Turbine ◽  
Low Reynolds Number ◽  
Small Wind Turbine ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

Experimental Comparison of DBD Plasma Actuators for Low Reynolds Number Separation Control

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Christopher R. Marks ◽  
Rolf Sondergaard ◽  
Mitch Wolff ◽  
Rich Anthony
Keyword(s):  
Reynolds Number ◽  
Low Reynolds Number ◽  
Turbine Blades ◽  
Plasma Actuators ◽  
Experimental Comparison ◽  
Separation Control ◽  
Suction Surface ◽  
Dbd Plasma ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

This paper presents experimental work comparing several Dielectric Barrier Discharge (DBD) plasma actuator configurations for low Reynolds number separation control. Actuators studied here are being investigated for use in a closed loop separation control system. The plasma actuators were fabricated in the U.S. Air Force Research Laboratory Propulsion Directorate’s thin film laboratory and applied to a low Reynolds number airfoil that exhibits similar suction surface behavior to those observed on Low Pressure (LP) Turbine blades. In addition to typical asymmetric arrangements producing downstream jets, one electrode configurations was designed to produce an array of off axis jets, and one produced a spanwise array of linear vertical jets in order to generate vorticity and improved boundary layer to freestream mixing. The actuators were installed on an airfoil and their performance compared by flow visualization, surface stress sensitive film (S3F), and drag measurements. The experimental data provides a clear picture of the potential utility of each design. Experiments were carried out at four Reynolds numbers, 1.4 × 105, 1.0 × 105, 6.0 × 104, and 5.0 × 104 at a-1.5 deg angle of attack. Data was taken at the AFRL Propulsion Directorate’s Low Speed Wind Tunnel (LSWT) facility.

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