Comparative study of two geometrically non-linear beam approaches for the coupled wind turbine system

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作者
Panteli, Anthoula N. [1 ]
Manolas, Dimitris I. [2 ]
Riziotis, Vasilis A. [3 ]
Spiliopoulos, Konstantinos V. [1 ]
机构
[1] School of Civil Engineering, Institute of Structural Analysis and Antiseismic Research, National Technical University of Athens, Heroon Polytechniou 9, Zografou Campus, Athens,GR 157-80, Greece
[2] iWind Renewables, PC, Kleisthenous 166 Gerakas, Athens,GR 153-44, Greece
[3] School of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, Zografou Campus, Athens,GR 157-80, Greece
关键词
Anisotropy - Turbine components - Turbomachine blades - Wind turbine blades;
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摘要
In the present work, the Simo-Reissner (SR) geometrically exact beam model is integrated into the comprehensive hydro-servo-aero-elastic code hGAST for the analysis of the coupled wind turbine system. The original SR beam implementation is upgraded to account for the fully populated stiffness matrix of the arbitrary-shaped cross-section, composed of inhomogeneous and anisotropic material. Predictions of blade loads and deflections by the new beam model, for static and aero-elastic/dynamic load cases, are compared against those of the default beam model used in hGAST, i.e. a linear Timoshenko beam model in which multi-body dynamic analysis is adopted in order to account for the geometrically non-linear effects due to high deflections. The almost perfect agreement of the grid independent solutions by the two models with benchmark cases suggests that the SR beam model has been consistently incorporated within hGAST and that both modeling options can accurately predict geometrically non-linear effects due to large deflections. Their main difference is that as non-linearities become more pronounced, the number of sub-bodies used in the multi-body analysis needs to increase in order to accurately capture these effects, while the higher fidelity SR beam model provides the same level of accuracy without increasing the spatial discretization. © 2022 Elsevier Ltd
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