LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller and a feedforward controller to a baseline controller. Realistic wind “measurements” are obtained using a detailed simulation of a LIDAR system. A full lifetime comparison shows the advantages of using the wind predictions to reduce wind turbine fatigue loads on the tower and blades as well as to limit the blade pitch rates. The results illustrate that the feedforward controller can be combined with a tower feedback controller to yield similar load reductions as the model predictive controller.

LIDAR (Light detection and ranging) systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This information can be used to improve the control of wind turbines. This paper compares a predictive feedforward control structure combined with common PI controllers to a baseline controller and to an H∞ approach showing the advantage of look-ahead control to reduce wind turbine loads. The control design is verified by simulations with a turbulent wind field and a full nonlinear model of the wind turbine.

In vivo aspiration experiments on human livers are analyzed and material parameters for a non-linear-viscoelastic constitutive model are determined. A novel procedure is applied for the inverse analysis that accounts for the initial tissue deformation in the experiment and for the non-homogeneity of liver tissue. A numerical model is used consisting of a surface layer (capsule) and an underlying non-linear-viscoelastic solid (parenchyma). The capsule is modeled as hyperelastic membrane using data from measurements on bovine and human tissue. In a two step optimization procedure the set of constitutive model parameters for the "average" response of liver parenchyma is obtained. The proposed model is in line with literature values of high strain rate elastic modulus obtained from dynamic elastography. The model can be used to predict the nonlinear, time dependent behavior of human liver in computer simulations related to surgery training and planning.