Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/70739
Title: Modelling the self-induced cycle-to-cycle variations in the aerodynamic blade loads of a yawed wind turbine
Authors: Elgammi, Moutaz (2017)
Keywords: Renewable energy resources
Wind turbines
Wind turbines -- Aerodynamics
Wind turbines -- Rotors
Fluid mechanics
Issue Date: 2017
Citation: Elgammi, M. (2017). Modelling the self-induced cycle-to-cycle variations in the aerodynamic blade loads of a yawed wind turbine (Doctoral dissertation).
Abstract: In the complex wind flow environment, horizontal axis wind turbines (HAWTs) experience three dimensional rotational and unsteady aerodynamic phenomena at the rotor blades sections. These highly unsteady three dimensional effects have a dramatic impact on the flow field close to a HAWT rotor, the aerodynamic load distributions on the blades, and the wake development downstream of the rotor. Unfortunately, there is still an incomplete understanding of the flow physics governing unsteady flow conditions, and hence the current theoretical models are often incapable of modelling the impact realistically. On the other hand, physical modelling of the wind turbine systems considering solely the average behaviour of the design variables and physical constants in the design process eliminates the underlining physics associated with the real dynamics of the system. These are characterised in the effects of the unstable process of dynamic stall vortex kinematics and the unsteady wake phenomena occurring in the close proximity of the rotor, amongst others. In this Ph.D. thesis, a different approach is considered for the effects of the cycle-to-cycle variations in the aerodynamic loads over multiple rotor rotations (cycles) for yawed rotors operating in a uniform and steady wind flow. This could enable new insight on modelling the direct influence of the blade on the three-dimensional flow.
Description: PH.D
URI: https://www.um.edu.mt/library/oar/handle/123456789/70739
Appears in Collections:Dissertations - FacEng - 2017

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