KIKI-net exhibits superior performance over state-of-the-art conventional algorithms in terms of restoring tissue structures and removing aliasing artifacts. The results demonstrate that KIKI-net is applicable up to a reduction factor of 3 to 4 based on variable-density Cartesian undersampling.

Link back to DTU OrbitCitation (APA): Kim, T., Hansen, A. M., & Branner, K. (2013 ABSTRACTIn this paper a new anisotropic beam finite element for composite wind turbine blades is developed and implemented into the aeroelastic nonlinear multibody code, HAWC2, intended to be used to investigate if use of anisotropic material layups in wind turbine blades can be tailored for improved performance such as reduction of loads and/or increased power capture. The element stiffness and mass matrices are first derived based on pre-calculated anisotropic beam properties, and the beam element is subsequently put into a floating frame of reference to enable full rigid body displacement and rotation of the beam. This derivation provides the mass and stiffness properties and the fictitious forces needed for implementation into HAWC2. The implementation is subsequently validated by running three validation cases which all show good agreement with results obtained by other authors. Further, a parametric study is conducted in order to investigate if the given anisotropic effect of the composite blade, bend-twist coupling effect, is able to be examined by the developed beam element in a multibody system or not. Two different coupled examples of bend-twist coupling for the blade of a 5MW fictitious wind turbine are considered. The two cases differ in the amount of bend-twist coupling introduced into the blade so that they produce 0.3deg and 1deg twist at the blade tip (towards feather), respectively, for a 1m flapwise tip deflection towards the tower. It is examined if the current structural model is able to capture the anisotropic effects in a multibody system.

In this paper, an aerodynamic model consisting of a lifting line-based trailed vorticity model and a blade element momentum (BEM) model is described. The focus is on the trailed vorticity model, which is based on the near wake model (NWM) by Beddoes and has been extended to include the effects of downwind convection and to enable a faster and more accurate computation of the induction, especially close to the blade root and tip. The NWM is introduced to model the detailed steady and unsteady induction from the first part of the trailed vorticity behind the individual rotor blades. The model adds a radial coupling between the blade sections and provides a computation of tip loss effects that depends on the actual blade geometry and the respective operating point. Moreover, the coupling of the NWM with a BEM theory-based far wake model is presented. To avoid accounting for the near wake induction twice, the induction from the BEM model is reduced by a coupling factor, which is continuously updated during the computation to ensure a good behavior of the model in varying operating conditions. The coupled near and far wake model is compared with a simple prescribed wake lifting line model, a BEM model and full rotor computational fluid dynamics (CFD) to evaluate the steady-state results in different cases. The model is shown to deliver good results across the whole operation range of the NREL 5-MW reference wind turbine.

A full-scale test was performed on a Vestas V27 wind turbine equipped with one active 70 cm long trailing edge flap on one of its 13 m long blades. Active load reduction could be observed in spite of the limited spanwise coverage of the single active trailing edge flap. A frequency-weighted model predictive control was tested successfully on this demonstrator turbine. An average flapwise blade root load reduction of 14% was achieved during a 38 minute test, and a reduction of 20% of the amplitude of the 1P loads was measured. A system identification test was also performed, and an identified linear model, from trailing edge flap angle to flapwise blade root moment, was derived and compared with the linear analytical model used in the model predictive control design model. Flex5 simulations run with the same model predictive control showed a good correlation between the simulations and the measurements in terms of flapwise blade root moment spectral densities, in spite of significant differences between the identified linear model and the model predictive control design model. Full-scale test of trailing edge flaps on a Vestas V27 wind turbine D. Castaignet et al.tabs, etc.) and actuators (trailing edge flaps, microtabs, boundary layer suction or blowing jets, plasma actuators, etc.) along the blades. A detailed overview of different smart rotor concepts is given by Barlas and van Kuik. 11 Trailing edge flaps on turbine blades have been investigated for several years, as part of this smart rotor concept. Computational fluid dynamics (CFD) simulations, 12 2D aeroelastic simulations 13,14 and 3D aeroelastic simulations [15][16][17] confirmed the high potential of trailing edge flaps to reduce flapwise blade root fatigue loads. Wind tunnel tests on a blade section 18-20 as well as on a scaled turbine 21 corroborated the ability of the trailing edge flaps to reduce loads. At last, in 2010, a full-scale test was carried out on the Vestas V27 turbine located at the Risø campus of Technical University of Denmark (DTU). Only open-loop controls were tested at that time, and no active fatigue load reduction was performed. 22 This paper shows the results from the latest tests made in 2011 on the same Vestas V27 demonstrator turbine. Those tests include active load reduction achieved with a frequency weighted model predictive control (MPC), 23 and derivation of a time-invariant linear model, from trailing edge flap angle to flapwise blade root moment, with a system identification method. 24,25 The first section of this paper describes the demonstrator wind turbine. The tests performed during this test campaign are developed in the second section. The simulation models are detailed in Section 4, and the results from the field tests are presented and compared with Flex5 simulations in Section 5.