1. Talents of tomorrow: Offshore wind energy – Research and technology impact

2. Breaking waves on the offshore wind turbine monopiles and the effects of boundary layer
Pitch by Amin Ghadirian, PhD student, DTU Wind Energy Ultimate Limit State load cases play an important role in the design process of the offshore wind turbine monopiles. Currently the method to calculate such loads is to replace the extreme waves in a specific sea state, with a stream function wave theory or a focused wave group. Despite of being fast and efficient these methods have disadvantages including being symmetric, periodic waves and only valid on flat bed. More advanced tools such as OceanWave3D and waves2Foam are introduced and validated in the literature. However, more extensive validation of these tools is still needed. In addition, the effect of more detailed physics like boundary layer, which these models are capable of taking into account, should be further investigated.

3. Development and validation of an engineering model for floating offshore wind turbines
Pitch by Antonio Pegalajar-Jurado, PhD student, DTU Wind Energy In the early stages of floater design for offshore wind deployment, each configuration needs to be assessed under several environmental conditions. Today, most time-domain numerical models for floating wind turbines are able to capture several physical phenomena, but they can also be computationally expensive. Hence, a quick model able to provide a broad overview of the system response is desirable. Here we present QuLAF, a frequency-domain, CPU-efficient model that captures 4 degrees of freedom: floater surge, heave and pitch, and tower deflection. Hydrodynamic and aerodynamics loads are precomputed, and the system response is obtained by solving the equations of motion in the frequency domain. The results from QuLAF are benchmarked against results from a state-of-the-art, time-domain model.

4. Developments and challenges in predictive maintenance
Pitch by Lorenzo Colone, PhD student, DTU Wind Energy O&M is one of the major cost driver for wind energy, especially offshore. The aim of this research is to develop cost-effective methodologies for wind farm O&M to help make the wind energy market more competitive. The content of the session will include new developments in the area of predictive maintenance with particular focus on strategies to improve the efficacy of current condition monitoring systems from SCADA data, early failure warning of main mechanical components and uncertainty handling, with examples of real world applications. The approach presented implements state-of-the-art intelligent systems based on data mining, machine learning and reliability engineering.