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ABSTRACT Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR‐assisted pitch control. This approach uses wind velocity measurements from a nacelle‐mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs) and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC + FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely, in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR‐assisted pitch control may allow for an up to 5% reduction in OpEx, increasing to up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR‐assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR‐assisted pitch control.

Abstract Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.

Due to increasing environmental concerns and global energy demand, the development of Floating Offshore Wind Turbines (FOWTs) is on the rise. FOWTs offer a promising solution to expand wind farm deployment into deeper waters with abundant wind resources. However, their harsh operating conditions and lower maturity level compared to fixed structures pose significant engineering challenges, notably in the design phase. A critical challenge is the time-consuming hydromechanics analysis traditionally done using computationally intensive Computational Fluid Dynamics (CFD) models. In this study, we introduce Artificial Intelligence-based surrogate models using state-of-the-art Machine Learning algorithms. These surrogate models achieve CFD-level accuracy (within 3% difference) while dramatically reducing computational requirements from minutes to milliseconds. Specifically, we build a surrogate model for characterizing the hydrodynamic response of a floating spar-type offshore wind turbine (including added mass, radiation damping matrices, and hydrodynamic excitation) using computationally efficient shallow Machine Learning models, optimizing the trade-off between computational efficiency and accuracy, based on data generated by a cutting-edge potential-flow code.

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration–OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.

The drive for the cost reduction of floating offshore wind turbine (FOWT) systems to the levels of fixed bottom foundation turbine systems can be achieved with creative design and analysis techniques of the platform with free-form curves to save numerical simulation time and minimize the mass of steel (cost of steel) required for design. This study aims to compare four parametric free-form curves (cubic spline, B-spline, Non-Uniform Rational B-Spline and cubic Hermite spline) within a design and optimization framework using the pattern search gradient free optimization algorithm to explore and select an optimal design from the design space. The best performance free-form curve within the framework is determined using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS technique shows the B-spline curve as the best performing free-form curve based on the selection criteria, amongst which are design and analysis computational time, estimated mass of platform and local shape control properties. This study shows that free-form curves like B-spline can be used to expedite the design, analysis and optimization of floating platforms and potentially advance the technology beyond the current level of fixed bottom foundations.

Integrating point absorber wave energy converters (PAWECs) and an offshore floating wind platform provide a cost-effective way of joint wind and wave energy exploitation. However, the coupled dynamics of the complicated hybrid system and its influence on power performance are not well understood. Here, a frequency-domain-coupled hydrodynamics, considering the constraints and the power output through the relative motion between the PAWECs and the semi-submersible platform, is introduced to optimize the size, power take-off damping, and layout of the PAWECs. Results show that the annual wave power generation of a PAWEC can be improved by 30% using a 90° conical or a hemispherical bottom instead of a flat bottom. Additionally, while letting the PAWECs protrude out the sides of the triangular frame of the platform by a distance of 1.5 times the PAWEC radius, the total power generation can be improved by up to 18.2% without increasing the motion response of the platform. The PAWECs can reduce the resonant heave motion of the platform due to the power take-off damping force. This study provides a reference for the synergistic use of wave and wind energy.

Aquaculture and marine renewable energy are two expanding sectors of the Blue Economy in Europe. Assessing the long-term environmental impacts in terms of eutrophication and noise is a priority for both the EU Water Framework Directive and the Marine Strategy Framework Directive, and cumulative impacts will be important for the Maritime Spatial Planning under the Integrated Maritime Policy. With the constant expansion of aquaculture production, it is expected that farms might be established further offshore in more remote areas, as high-energy conditions offer an opportunity to generate more power locally using Marine Renewable Energy (MRE) devices. A proposed solution is the co-location of MRE devices and aquaculture systems using Multi-Purpose Platforms (MPPs) comprising offshore wind turbines (OWTs) that will provide energy for farm operations as well as potentially shelter the farm. Disentangling the impacts, conflicts and synergies of MPP elements on the surrounding marine ecosystem is challenging. Here we created a high-resolution spatiotemporal Ecospace model of the West of Scotland, in order to assess impacts of a simple MPP configuration on the surrounding ecosystem and how these impacts can cascade through the food web. The model evaluated the following specific ecosystem responses: (i) top-down control pathways due to distribution changes among top-predators (harbor porpoise, gadoids and seabirds) driven by attraction to the farming sites and/or repulsion/killing due to OWT operations; (ii) bottom-up control pathways due to salmon farm activity providing increasing benthic enrichment predicated by a fish farm particle dispersal model, and sediment nutrient fluxes to the water column by early diagenesis of organic matter (recycled production). Weak responses of the food-web were found for top-down changes, whilst the results showed high sensitivity to increasing changes of bottom-up drivers that cascaded through the food-web from primary producers and detritus to pelagic and benthic consumers, respectively. We assessed the sensitivity of the model to each of these impacts and the cumulative effects on the ecosystem, discuss the capabilities and limitations of the Ecospace modeling approach as a potential tool for marine spatial planning and the impact that these results could have for the Blue Economy and the EU’s New Green Deal.

The heaving wave energy converter (WEC) is one typical type of point absorber WECs with high energy conversion efficiency but significantly affected by the viscous effect. It is widely known that the bottom shape of such WECs plays an important role in influencing the viscosity, so a detailed qualitative investigation is essential. Here a numerical study is performed for the influence of bottom shape on motion response and energy conversion performance of a heaving WEC. The numerical model is developed based on the potential flow theory with a viscous correction in the frequency domain. Cylindrical WECs with flat, cone, and hemispherical bottoms and the same displacement are considered. WECs with larger diameter-to-draft ratios (DDRs) are found to experience a relatively smaller viscous effect and achieve effective energy conversion in a broader frequency range. With the same DDR, the flat bottom has the most considerable viscous effect, following by the cone bottom with conical angles 90° and the hemispherical bottom. When the DDR is relatively small, the hemispherical bottom had the best energy conversion performance. Similarly, when the DDR is relatively large, the energy conversion performance of the floater with a hemispherical bottom and a cone bottom with 90° is better, while that with the flat bottom is the worst.

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

Expansion of marine aquaculture into more remote areas will likely accelerate over the next decade. Integrating Marine Renewable Energy (MRE) generation technologies (e.g., wind turbines) into remote, off-grid aquaculture sites will reduce reliance on fossil fuels by allowing localised low-carbon power generation, but may also result in novel environmental pressures. In this study, we undertook a thought experiment to assess the potential for increased collision risks to local marine and coastal bird species of integrating small wind turbines (4 units; combined capacity of 200 MWh) into a generalised marine fish farm in western Scotland (UK). Potential risks to bird species were assessed using a bespoke Sensitivity Index (SI) based on 12 factors, including population size, adult survival rate, UK conservation status, flight manoeuvrability, nocturnal flight activity, habitat preference, sensitivity to wind farms, attraction to fish farms for feeding and/or resting, and attraction to other marine anthropogenic structures/activities. SI scores varied substantially between species, but large gulls (Larus sp.) and European shag (Phalacrocorax aristotelis) were expected to be at the greatest potential risk. The general lack of information on interactions between birds and fish farms represented a significant knowledge gap, and greater focus on these interactions is needed to improve future risk assessments.

The complexity of floating offshore wind turbine (FOWT) systems, with their coupled motions, aero-hydro-servo-elastic dynamics, as well as non-linear system behavior and components, makes modeling and simulation indispensable. To ensure the correct implementation of the multi-physics, the engineering models and codes have to be verified and, subsequently, validated for proving the realistic representation of the real system behavior. Within the IEA (International Energy Agency) Wind Task 23 Subtask 2 offshore code-to-code comparisons have been performed. Based on these studies, using the OC3 phase IV spar-buoy FOWT system, the Modelica for Wind Turbines (MoWiT) library, developed at Fraunhofer IWES, is verified. MoWiT is capable of fully-coupled aero-hydro-servo-elastic simulations of wind turbine systems, onshore, offshore bottom-fixed, or even offshore floating. The hierarchical programing and multibody approach in the object-oriented and equation-based modeling language Modelica have the advantage (over some other simulation tools) of component-based modeling and, hence, easily modifying the implemented system model. The code-to-code comparisons with the results from the OC3 studies show, apart from expected differences due to required assumptions in consequence of missing data and incomplete information, good agreement and, consequently, substantiate the capability of MoWiT for fully-coupled aero-hydro-servo-elastic simulations of FOWT systems.

The issue of the longitudinal stability of a WIG vehicle has been a very critical design factor since the first experimental WIG vehicle has been built. A series of studies had been performed and focused on the longitudinal stability analysis. However, most studies focused on the longitudinal stability of WIG vehicle in cruise phase, and less is available on the longitudinal static stability requirement of WIG vehicle when hydrodynamics are considered: WIG vehicle usually take off from water. The present work focuses on stability requirement for longitudinal motion from taking off to landing. The model of dynamics for a WIG vehicle was developed taking into account the aerodynamic, hydrostatic and hydrodynamic forces, and then was analyzed. Following with the longitudinal static stability analysis, effect of hydrofoil was discussed. Locations of CG, aerodynamic center in pitch, aerodynamic center in height and hydrodynamic center in heave were illustrated for a stabilized WIG vehicle. The present work will further improve the longitudinal static stability theory for WIG vehicle.

The necessity of producing more electricity from renewable sources has been driven predominantly by the need to prevent irreversible climate chance. Currently, industry is looking towards floating offshore wind turbine solutions to form part of their future renewable portfolio. However, wind turbine loads are often increased when mounted on a floating rather than fixed platform. Negative damping must also be avoided to prevent tower oscillations. By presenting a turbine actively pitching-to-stall, the impact on the tower fore−aft bending moment of a blade with back twist towards feather as it approaches the tip was explored, utilizing the time domain FAST v8 simulation tool. The turbine was coupled to a floating semisubmersible platform, as this type of floater suffers from increased fore−aft oscillations of the tower, and therefore could benefit from this alternative control approach. Correlation between the responses of the blade’s flapwise bending moment and the tower base’s fore−aft moment was observed with this back-twisted pitch-to-stall blade. Negative damping was also avoided by utilizing a pitch-to-stall control strategy. At 13 and 18 m/s mean turbulent winds, a 20% and 5.8% increase in the tower axial fatigue life was achieved, respectively. Overall, it was shown that the proposed approach seems to be effective in diminishing detrimental oscillations of the power output and in enhancing the tower axial fatigue life.

System identification of offshore floating platforms is usually performed by testing small-scale models in wave tanks, where controlled conditions, such as still water for free decay tests, regular and irregular wave loading can be represented. However, this approach may result in constraints on model dimensions, testing time, and costs of the experimental activity. For such reasons, intermediate-scale field modelling of offshore floating structures may become an interesting as well as cost-effective alternative in a near future. Clearly, since the open sea is not a controlled environment, traditional system identification may become challenging and less precise. In this paper, a new approach based on Frequency Domain Decomposition (FDD) method for Operational Modal Analysis is proposed and validated against numerical simulations in ANSYS AQWA v.16.0 on a simple spar-type structure. The results obtained match well with numerical predictions, showing that this new approach, opportunely coupled with more traditional wave tanks techniques, proves to be very promising to perform field-site identification of the model structures.