Hybrid Modelling of Loads and Structural Response on a Floating Offshore Wind Turbine

Partners: Newcastle University

As wind turbines move further offshore, additional hydrological and meteorological challenges make fixed foundation turbines less feasible. The goal of this project is to create a high-fidelity hybrid floating offshore wind turbine (FOWT) aero-hydro-structural dynamic performance predictive computer model. Accurate predictions of structural loads will be used to optimise the design of next- generation FOWTs . The development of this model will also provide economic benefits and contribute to the global Net Zero 2050 target. This work will focus on the realisation of a novel data assimilation algorithm, underpinned by a computational fluid dynamics output, a numerical structure model and experimental data. Using this model to accurately estimate and predict structural responses to wind, waves, and currents on FOWTs will support optimal FOWT design and life cycle extension.

Development of a Lab-scale Wind Turbine Equipped with Smart Rotor Blade Technology for Wind Turbine Wake Control

Partners: Aura CDT

Description: Wind turbine wakes may affect the overall efficiency of a wind farm as the wake from a prevailing wind turbine can lower the relative wind speed seen by following turbines. The motivation of this project is to investigate a new method to control the wake of a wind turbine using Trailing Edge Flaps (TEFs) on a wind turbine. To achieve this, we propose to design, build, and test a lab-scale wind turbine equipped with TEFs at the tip of the blades. The objective of this study is to manipulate the characteristic tip vortex found in the near-wake of a wind turbine by actuating the TEF at different prescribed positions. Wind tunnel testing using Stereo-Particle Image Velocimetry techniques will be used to visualise and quantify the changes in the near-wake.

Analytical Modelling of Wind Turbine Wakes in the Turbulent Atmospheric Boundary Layer

Partners: Aura CDT

Wind turbine wakes negatively affect wind farm annual energy production by reducing the relative wind speed at downstream wind turbines. Fatigue loading caused by wake turbulence may also result in additional maintenance being necessary. Wake studies are complicated by the wide range of dynamic parameters including turbine characteristics, wind farm scale and geometry, and atmospheric conditions affect wake development. Finding a balance between accuracy and simplicity is required to efficiently predict near and far wake flow dynamics. This project aims to develop an analytical model of turbulence in wind farm wakes with the aid of data from wind tunnel studies to help optimise wind farm design and operation. Further development of the model will incorporate atmospheric boundary layer stability.

Exploiting Modern Image Processing in Surface Flow Visualization

Secondary losses in at the endwalls of turbomachinery components can lead to reduced efficiency. To improve the efficiency, it is first necessary to understand the aerodynamic flow properties. An image processing algorithm has been enhanced to efficiently detect and extract data from endwall streamlines visualized through oil film surface flow visualization. The optimization of the algorithm has improved its accuracy in capturing fluid dynamics features, making the extraction of information from complex flow fields more reliable. The development of this algorithm provides researchers with a powerful tool to delve into the motion and distribution of endwall streamlines, opening new possibilities for studies in fluid dynamics and engineering.

Active flow and noise control by a low-cost smart device (Aficose)

Partners: Southern University of Science and Technology, China

Description: Owing to unstable flow separation most engineering structures shed vortices. Periodic oscillations result in structural fatigue and aeroacoustic noise generation. While vibration strength and noise generally increase with flow speed, they can be efficiently suppressed by actively controlling the location of flow separation. Polygonal structures provide a solution owing to their fixed separation points at apexes. Preliminary studies have shown that the aeroacoustic response is non-linear with the order of the polygon and strongly dependant on the orientation facing the flow, and that some geometries, e.g. pentagonal, are superior in drag, vibration, and noise reduction. By twisting the structures, flow separation points can be controlled to induce destructive vortex interactions, thus supressing structural vibration and noise generation. This unique characteristic is being used to develop a smart device having dynamic twist control based on an instantaneous flow loading feedback signal. This device can be used alone or attached to any flow structures to achieve effective flow and noise control.

Vortex Induced Vibration for dynamic cables on offshore wind structures

Partners: PDL group

Description: This project investigates the nature of vortex-induced vibrations in the context of offshore subsea cables, specifically the case of an offshore wind turbine array power cable free to vibrate in the wake of a large diameter fixed monopile. The effects of three parameters have been considered: stiffness constant, free-stream current, and relative positioning of the cable in the wake. Two-dimensional computational fluid dynamics has been used to investigate the interactions of array cables in the wind turbine wake. It has been shown that large body wakes significantly contribute to vibration, particularly at reduced velocities, and significantly add to the complexity of the response.

Instabilities in the Wake of Polygonal Cylinders

The flow past polygonal cylinders has many engineering applications, such as, wind flowing past skyscrapers or bridges, or water flowing past offshore structures. Polygonal cylinders can also be used to extract hydropower from the vortex induced vibrations caused by the wake instabilities. This project will investigate the effect of the number of sides and angle of incidence on the flow instabilities in the wake of polygonal cylinders. A sensitivity analysis of changes to primary and secondary instabilities in the wake caused by variations in cylinder side number and incidence angle will be performed using 2D computational fluid dynamics. Particle image velocimetry experiments will be used to validate computational results.

CONFLOWS – Control of FLOating Wind Farms with Wake Steering (phase 2)

Partners: DNV, NREL, Teesside University

The scalability of floating wind is vital to the UK’s energy security and the future of offshore power generation. The transition from shallow waters to deep waters with floating offshore wind turbines (FOWTs) opens many new opportunities in the offshore wind sector. According to ORE Catapult, FOWTs could add £43.6 billion of gross value to the UK economy by 2050. The performance of floating wind farms, wake interactions and flow control in such complex environments remains an unexplored problem. This project brings a team of academic and industry experts to develop innovative control strategies for FOWTs by investigating a hybrid (vertical and horizontal) misalignment of the turbines as a new wake steering strategy. This reduces the cost of floating wind projects and strengthens the UK’s position as a leader in offshore wind.

Systematic Prediction of Wind Farm Wakes: An Emerging Challenge in the Offshore Wind Sector

Offshore wind has grown exponentially over the past decade and is expected to become one of the major sources of renewables in both the UK and Sweden within the next decade. To meet this target, many new offshore wind farms will be installed in finite offshore areas with favourable conditions. This will raise an important question about the interaction of neighbouring wind farms with each other, which is far from being well understood. In this collaborative project, we aim to pool our knowledge and resources to systematically study and model this problem using our cutting-edge experimental, numerical and theoretical tools. Outcomes of this project will improve our ability to predict and optimise performance of offshore wind plants.