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Novel covalently bonded nanoscale mesoporous silica (NMS)/polyimide (ODPA−BAPP) nanocomposites were synthesized through the bridge 3-aminopropyltrimethoxysilane. Abundant reactive amino functional groups inside the mesopore channels of NMS can interconnect with numerous polyimide main chains, forming multilinked and complex NMS/PI networks. These multilinkages, NMS inherent nanoporosity, and the uniform dispersion of NMS in polyimide significantly improve thermal stability, mechanical properties, elongation, moisture absorption, and dielectric constant of these novel materials over those of neat polyimide. For instance, the storage modulus is increased by 160%, the decomposition temperature by 35.7 °C, and the glass transition temperature by 14.5 °C. The maximum elongation increases by 66%, hardly observed in other polyimide nanocomposites, probably because the covalent bond and NMS nanopore can absorb more energy than pristine poyimide before failure. The nanoporosity of the core of the NH2-NMS material within the nanocomposite film is most likely responsible for the decrease of dielectric constant by 10%.

Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system, different rotor sizes, and wind evolution. The method is compared to real measurement data with promising results. In addition, examples depict how this model can be used to design an optimal controller and how the configuration of a lidar system is optimized for a given turbine to improve the correlation.

Existing Floating Offshore Wind Turbine (FOWT) platforms are usually designed using static or rigid-body models for the concept stage and, subsequently, sophisticated integrated aero-hydro-servo-elastic models, applicable for design certification. For the new technology of FOWTs, a comprehensive understanding of the system dynamics at the concept phase is crucial to save costs in later design phases. This requires low-and medium-fidelity models. The proposed modeling approach aims at representing no more than the relevant physical effects for the system dynamics. It consists, in its core, of a flexible multibody system. The applied Newton-Euler algorithm is independent of the multibody layout and avoids constraint equations. From the nonlinear model a linearized counterpart is derived. First, to be used for controller design and second, for an efficient calculation of the response to stochastic load spectra in the frequency-domain. From these spectra the fatigue damage is calculated with Dirlik's method and short-term extremes by assuming a normal distribution of the response. The set of degrees of freedom is reduced, with a response calculated only in the two-dimensional plane, in which the aligned wind and wave forces act. The aerodynamic model is a quasistatic actuator disk model. The hydrodynamic model includes a simplified radiation model, based on potential flow-derived added mass coefficients and nodal viscous drag coefficients with an approximate representation of the second-order slow-drift forces. The verification through a comparison of the nonlinear and the linearized model against a higher-fidelity model and experiments shows that even with the simplifications, the system response magnitude at the system eigenfrequencies and the forced response magnitude to wind and wave forces can be well predicted. One-hour simulations complete in about 25 seconds and even less in the case of the frequency-domain model. Hence, large sensitivity studies and even multidisciplinary optimizations for systems engineering approaches are possible.

Abstract-In this work a nonlinear model predictive controller with individual pitch control for a floating offshore wind turbine is presented. An aerodynamic model of the collective pitch control approach is extended by describing pitching and yawing moments based on rotor disk theory. This extension is implemented in a reduced nonlinear model of the floating wind turbine including disturbance preview of wind speed, linear vertical and horizontal wind shear, and wave height to compute optimal input trajectories for the individual pitch control inputs and the generator torque. An extended cost functional for individual pitch control is proposed based on the collective pitch control approach. The controller is evaluated in aero-servohydro-elastic simulations of a 5 MW reference wind turbine disturbed by a three-dimensional stochastic turbulent wind field. Results show a significant blade fatigue load reduction compared to a baseline controller through minimizing yawing and pitching moments on the rotor hub while maintaining the advantages of the model predictive control approach with collective pitch control.