The paper deals with small wind turbines for grid-independent or small smart grid wind turbine systems. Not all small turbine manufacturers worldwide have access to the engineering capacity for designing an efficient turbine. The objective of this work is to provide an easy-to-handle integrated design and performance prediction method for wind turbines and to show exemplary applications.
The underlying model for the design and performance prediction method is based on an advanced version of the well-established blade-element-momentum theory, encoded in MATLAB™. Results are (i) the full geometry of the aerodynamically profiled and twisted blades which are designed to yield maximum power output at a given wind speed and (ii) the non-dimensional performance characteristics of the turbine in terms of power, torque and thrust coefficient as a function of tip speed ratio. The non-dimensional performance characteristics are the basis for the dimensional characteristics and the synthesis of the rotor to the electric generator with its load.
Two parametric studies illustrate typical outcomes of the design and performance prediction method: A variation of the design tip speed ratio and a variation of the number of blades. The predicted impact of those parameters on the non-dimensional performance characteristics agrees well with common knowledge and experience.
Eventually, an interplay of various designed turbine rotors and the given drive train/battery charger is simulated. Criterions for selection of the rotor are the annual energy output, the rotor speed at design wind speed as well as high winds, and the axial thrust exerted on the rotor by the wind. The complete rotor/drive train//battery charger assembly is tested successfully in the University of Siegen wind tunnel.
Citation: Stephen K. Musau, Kathrin Stahl, Kevin Volkmer, Nicholas Kaufmann, Thomas H. Carolus. A design and performance prediction method for small horizontal axis wind turbines and its application[J]. AIMS Energy, 2021, 9(5): 1043-1066. doi: 10.3934/energy.2021048
The paper deals with small wind turbines for grid-independent or small smart grid wind turbine systems. Not all small turbine manufacturers worldwide have access to the engineering capacity for designing an efficient turbine. The objective of this work is to provide an easy-to-handle integrated design and performance prediction method for wind turbines and to show exemplary applications.
The underlying model for the design and performance prediction method is based on an advanced version of the well-established blade-element-momentum theory, encoded in MATLAB™. Results are (i) the full geometry of the aerodynamically profiled and twisted blades which are designed to yield maximum power output at a given wind speed and (ii) the non-dimensional performance characteristics of the turbine in terms of power, torque and thrust coefficient as a function of tip speed ratio. The non-dimensional performance characteristics are the basis for the dimensional characteristics and the synthesis of the rotor to the electric generator with its load.
Two parametric studies illustrate typical outcomes of the design and performance prediction method: A variation of the design tip speed ratio and a variation of the number of blades. The predicted impact of those parameters on the non-dimensional performance characteristics agrees well with common knowledge and experience.
Eventually, an interplay of various designed turbine rotors and the given drive train/battery charger is simulated. Criterions for selection of the rotor are the annual energy output, the rotor speed at design wind speed as well as high winds, and the axial thrust exerted on the rotor by the wind. The complete rotor/drive train//battery charger assembly is tested successfully in the University of Siegen wind tunnel.
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Stephen K. Musau, Kathrin Stahl, Kevin Volkmer, Nicholas Kaufmann, Thomas H. Carolus. A design and performance prediction method for small horizontal axis wind turbines and its application[J]. AIMS Energy, 2021, 9(5): 1043-1066. doi: 10.3934/energy.2021048
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