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Part IV: Blade Geometry Optimization

This paper explores advanced optimization techniques in blade geometry design for wind turbines, focusing on maximizing energy output while considering various constraints and objectives. Genetic algorithms and other methods are discussed in detail, providing insights into achieving optimal designs for improved energy efficiency. The study presents a comprehensive overview of blade optimization approaches and methodologies, emphasizing the importance of robust techniques for complex problems with multiple objectives. The history and evolution of blade design processes, along with new features under development, are also highlighted.

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Part IV: Blade Geometry Optimization

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  1. Part IV: Blade Geometry Optimization Philippe Giguère Graduate Research Assistant Department of Aeronautical and Astronautical Engineering University of Illinois at Urbana-Champaign Steady-State Aerodynamics Codes for HAWTs Selig, Tangler, and Giguère August 2, 1999  NREL NWTC, Golden, CO

  2. Outline • Approaches to Optimization • Blade Geometry Optimization • Optimization Methods for HAWTs • PROPGA

  3. Approaches to Optimization • Iterative Approach • Use direct or inverse design method • Designer knowledge important • Inverse design can lead directly to an optimum blade for maximum energy (variable-speed HAWTs)

  4. “Black Box” Optimization Method Design Parameters and Constraints Optimum Design Analysis Method • Direct Optimization • Many optimization techniques available • Nature of problem dictates the optimization method

  5. “Black Box” Warning! • An optimization method will take advantage of the weaknesses of the analysis tool(s) and problem formulation • Optimization technique must be implemented with care • Know your analysis tool(s)

  6. Blade Geometry Optimization • Many Design Variables (continuous and discrete) • Chord and twist distributions • Blade pitch • Airfoils • Turbine configuration and control systems • Competing Objectives • Maximum energy • Minimum cost • Airfoil data not always smooth (“noisy” problem) • Complex problem often with many local optima • Need a robust optimization technique

  7. Optimization Methods for HAWTs • Garrad Hassan (Jamieson and Brown, 1992) • Simplex method for aerodynamic design • Optimize chord and twist for maximum energy • University of Athens (Belessis et al., 1996) • Genetic algorithm for aerodynamic design with limited structural constraints • Parameterized airfoil data • Risø (Fuglsang and Madsen, 1996) • Sequential linear programming with method of feasible directions for aerodynamic design • Structural, fatigue, noise, and cost considerations

  8. PROPGA • Description • Genetic-algorithm based optimization method • Optimize blade geometry given set of constraints • Possible design variables • chord and twist distributions, blade pitch, rotor diameter, number of blades, etc. • Typical constraints • Operating conditions, rated power, airfoil distribution, steady-blade loads, and all fixed design variables • Uses PROPID to evaluate the blade designs and achieve inverse design specifications • Initially developed to optimize blades for max. energy

  9. What are Genetic Algorithms (GAs)? • Robust search technique based on the principle of the survival of the fittest • Work with a coding of the parameters • Simplified example: blade chord & pitch is 101001 • Search from sub-set (population) of possible solutions over a number of generations • Use objective function information (selection) • Use probabilistic transition rules based on and genetic operations • crossover: 111|1&000|0gives1110&0001 • mutation: 1 to 0 or 0 to 1 (small probability) • etc.

  10. Step 1 Coding of the Parameters Step 2 Formation of Initial Population Step 3 Analysis of Each Individual of Population Step 4 Creation of Mating Pool Reproduction Operator Step 5 Crossover Operator No New Population Offsprings Step 6 Mutation Operator Is this the last generation? Yes Optimization Process Complete • PROPGA Flowchart

  11. Blade Design Example (Maximize Energy Capture) • Evolution of the chord and twist distributions

  12. History of the energy production • Population of 100 blades over 100 generations

  13. Blades Designed with PROPGA/PROPID • Tapered/twisted blade for the NREL Combined Experiment Rotor • WindLite 8-kW wind turbine • Replacement blade for the Jacob’s 20-kW HAWT • Twist distribution of the replacement blade for the US Windpower 100-kW turbines

  14. New Features (Under Development) • Airfoil selection • Advanced GA operators • Structural modeling • Weight/cost modeling for main components • Rotor, hub, drivetrain, and tower • Minimize cost of energy • Multi-objectives (get tradeoff curves) • Paper at the ASME Wind Energy Symposium, Reno, NV, January 2000

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