Review of aeroelasticity for wind turbine: Current status, research focus and future perspectives

Cardozo

et al. 2019

Engineering Structures

The total wind power capacity installed in the world has substantially grown during the last few years, mainly due to the increasing number of horizontal axis wind turbines (HAWT). Consequently, a big effort was employed to increase HAWT's power capacity, which is directly associated to the size of blades. Then, novel designs of blades may lead to very flexible structures, susceptive to large deformation, not only during extreme events, but also for operational conditions. In this context, this thesis aims to compare two geometrically nonlinear structural modeling approaches that handle large deformation of blade structures: 3D geometrically-exact beam and shell finite element models. Regarding the beam model, due to geometric complexity of typical cross-sections of wind turbine blades it is adopted a theory that allows creation of arbitrary multicellular cross-sections. Two typical blade geometries are tested, and comparisons between the models are done in statics and dynamics, always inducing large deformation and exploring the accuracy limits of beam models, when compared to shells. Results showed that the beam and shell models present very similar behavior, except when violations occur on the beam formulation hypothesis, such as when shell local buckling phenomena takes place.

Section: Introductionmentioning

confidence: 99%

Modeling wind turbine blades by geometrically-exact beam and shell elements: A comparative approach

Cardozo

et al. 2019

Engineering Structures

“…Wind energy provides an environmentally friendly option .It can also provide sustainable development security when fossil fuels are reduced [1]. Nowadays, offshore wind turbine is one of the sources of wind energy in the world.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing in the Design and Optimization of Tuned Liquid Column–Gas Damper for Use in Offshore Wind Turbines

Dezvareh

2019

ijcoe

Tuned liquid column gas damper is a new type of energy absorber that can mitigate the vibrations of structures if their frequency and mass parameters are well tuned. Since this damper has recently been introduced and its behaviour in certain structures such as offshore oil platforms and wind turbines has already been tested, a suitable and accurate method is required to identify these optimal parameters. Therefore, considering the complexity of loads exerted on wind turbines in seas (wave and wind loads), in present study attempts are made to use a new artificial neural network approach to obtain optimal tuned liquid column-gas damper (TLCGD) parameters for mitigation of wind turbine vibrations. First fixed offshore wind turbines at various depths are designed in the MATLAB coding environment. After obtaining the stiffness, damping and mass matrices of the structures, the program enters the Simulink, and the wind turbine structure along with the TLCGD is exposed to different wave-wind load combinations within reasonable range of damper parameters. The neural network training is launched based on available statistical data of the offshore wind turbine with different heights as well as different frequency and mass ratios of the damper. According to this method, the percentage of errors found in the neural network outputs was negligible compared to the actual results obtained from the analysis in Simulink (even for inputs that stood outside the training range of the neural network). The mean error percentage, the standard deviation and the effective value of the neural network with actual values are below 10% for all three types of the structure. Finally, the method presented in this study can be used to obtain optimal parameters of the TLCGD for all kinds of offshore wind turbines at different depths of the sea, which leads to the optimal design of this damper to reduce the vibrations of wind turbines under wave and wind load pressures.

“…With the advent of large, flexible blades, the tools required to design and analyze these turbines also need to be updated according to the current and future state of affairs. A wind turbine aeroelastic simulation code consists of several modules that model different aspects of wind turbine physics (aerodynamics, structures, pitch/torque control modules) (Hansen, 2000;Zhang and Huang, 2011). Each of these modules is based on a set of assumptions that may or may not be valid as the size and flexibility of the turbine increases.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these modules is based on a set of assumptions that may or may not be valid as the size and flexibility of the turbine increases. For example, torsional degree of freedom (DOF) becomes increasingly important for larger, more flexible blades, especially those that use sweep-twist or bend-twist coupling for passive load alleviation (Rasmussen et al, 2003;Zhang and Huang, 2011). The next generation of aeroelastic codes should be capable of modeling such structures.…”

Section: Introductionmentioning

confidence: 99%

A Validation and Code-to-Code Verification of FAST for a Megawatt-ScaleWind Turbine with Aeroelastically Tailored Blades

Guntur¹,

Jonkman²,

Sievers³

et al. 2017

Preprint

Abstract. This paper presents validation and code-to-code verification of the latest version of the U.S. Department of Energy, National Renewable Energy Laboratory wind turbine aeroelastic engineering simulation tool, FAST v8. A set of 1,141 test cases, for which experimental data from a Siemens 2.3 MW machine have been made available and were in accordance with the International Electrotechnical Commission 61400-13 guidelines, were identified. These conditions were simulated using FAST as well as the Siemens in-house aeroelastic code, BHawC. This paper presents a detailed analysis comparing results from FAST with those from BHawC as well as experimental measurements, using statistics including the means and the standard deviations along with the power spectral densities of select turbine parameters and loads. Results indicate a good agreement among the predictions using FAST, BHawC, and experimental measurements. These agreements are discussed in detail in this paper, along with some comments regarding the differences seen in these comparisons relative to the inherent uncertainties in such a model-based analysis.