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Nonlinear modeling of tuned liquid dampers (TLDs) in rotating wind turbine blades for damping edgewise vibrations

机译:旋转风轮机叶片中用于阻尼边缘振动的调谐液体阻尼器(TLD)的非线性建模

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Tuned liquid dampers (TLDs) utilize the sloshing motion of the fluid to suppress structural vibrations and become a natural candidate for damping vibrations in rotating wind turbine blades. The centrifugal acceleration at the tip of a wind turbine blade can reach a magnitude of 7-8g. This facilitates the use of a TLD with a relatively small fluid mass and with feasible geometric dimensions to mitigate the lightly-damped edgewise vibrations effectively. In the present paper, modal expansions are carried out directly on the velocity field and the free surface of the sloshing liquid in the rotating coordinate system. A formulation has been proposed leading to coupled nonlinear ordinary differential equations, which have been obtained through the Galerkin variational approach together with the modal expansion technique. Two models, with one sloshing mode and three sloshing modes, have been studied in the numerical simulation. It is shown that the one-mode model is able to predict the sloshing force and the damped structural response accurately, since the primary damping effect on the structure is achieved by the first sloshing mode of the fluid. Although it is unable to predict the fluid free-surface elevation equally well, the one-mode model can still be utilized for the design of TLD. Parametric optimization of the TLD is carried out based on the one-mode model, and the optimized damper effectively improves the dynamic response of wind turbine blades. (C) 2015 Elsevier Ltd. All rights reserved.
机译:调谐式液体阻尼器(TLD)利用流体的晃动运动来抑制结构振动,并成为阻尼旋转风力涡轮机叶片中振动的自然选择。风力涡轮机叶片尖端的离心加速度可以达到7-8g。这有助于使用相对较小的流体质量和可行的几何尺寸的TLD,以有效地减轻轻微阻尼的边向振动。在本文中,模态展开直接在旋转坐标系中的速度场和晃动液体的自由表面上进行。已经提出了导致耦合的非线性常微分方程的公式,该方程是通过Galerkin变分方法和模态展开技术获得的。在数值模拟中研究了两种模型,一种为晃动模式,另一种为三种晃动模式。结果表明,单模模型能够准确地预测晃荡力和结构的阻尼响应,因为对流体的第一阻尼作用是通过流体的第一晃荡模式实现的。尽管无法很好地预测流体自由表面的高度,但单模模型仍可用于TLD的设计。 TLD的参数优化基于单模模型进行,优化的阻尼器有效地提高了风力涡轮机叶片的动态响应。 (C)2015 Elsevier Ltd.保留所有权利。

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