Floating Offshore Wind Turbines (FOWT) can harness the abundant offshore wind resource at reduced installation requirements. However, a further decrease in the development risks through higher confidence in the design and analysis methods is needed. The dynamic behaviour of FOWT systems is complex due to the strong interactions between the large translational and rotational motions and the diverse loads, which poses a challenge. While the methods to study the FOWT's general responses are well established, there are no methods to describe the highly complex time-dependent rotational motion patterns of FOWT. For a rigid body in general plane motion, an Instantaneous Centre of Rotation (ICR) can be identified as a point at which, at a given moment, the velocity is zero. However, it is common to assume a centre of rotation fixed in space and time, arbitrarily set at the centre of floatation or gravity. Identification of the ICR is crucial as it may lead to better motion reduction methods and can be leveraged to improve the designs. This includes better-informed fairlead placement and the reduction of aerodynamic load variability. In this paper, we propose a two-fold approach for the identification of the ICR: an analytical solution in the initial static equilibrium position, and a time-domain numerical approach for dynamic analysis in regular and irregular waves to understand the motion patterns and ICR sensitivity to environmental conditions. Results show that the ICR of FOWT depends on wave frequency and, at low frequencies, on wave height, due to the nonlinear viscous drag and mooring loads. An unexpected but interesting result is that the surge-heave-pitch coupling introduced by the mooring system leads to a dynamic phenomenon of signal distortion known as ”clipping” in the nonlinear audio signal processing area, which, through the introduction of higher harmonics, is responsible for the ICR sensitivity to motion amplitude.@en

The development of novel energy technologies to meet net zero carbon emission is essential in the provision of solutions to realize an increasing worldwide demand for renewable energy. Floating Offshore Wind Turbine (FOWT) is one of the emerging technologies to exploit the vast wind resources available in deep waters within the offshore wind sector. However, as a result of the complexity of a FOWT system, bringing FOWT technology up to speed requires a detailed understanding of the different disciplines within the system and the relationship between the FOWT system and the dynamics of the marine environment; hence, the need for Multidisciplinary Design Analysis and Optimization (MDAO) of the system. This paper reviews the MDAO of FOWT substructures/platforms proposed in the literature. This review covers an overview of floating offshore wind turbine substructures’ concepts, the design using geometric shape parameterization techniques and the analysis approaches (time and frequency domain) for response assessment of the FOWT system. It also provides a comprehensive review of MDAO frameworks for FOWT substructures. Regarding the optimization aspect, a review of some optimization algorithms used for floating offshore wind turbine substructure is provided, i.e., from the global search heuristic and meta-heuristics algorithms to the local search gradient-based optimization algorithms. This work further identifies the research gaps in MDAO for FOWT substructures. The main proposed future research areas to address these gaps are: increasing design space richness by adopting more advanced parametrization techniques to represent the platform geometry (and other characteristics), utilize surrogate/meta models to replace the most computationally expensive high-fidelity models needed for quick sensitivity studies before detailed analyses on selected models are conducted, and exploring the upscaling of the geometric design parameters of an optimal shape parameterized FOWT platforms derived from existing designs which can be coupled with new generation highly rated and heavier turbines.@en

As new offshore wind development sites move further from shore and existing sites enter their post-subsidy operating period, it is expected that operational expenditure (OpEx) will increase. In order to overcome these challenges, a more flexible and cost-effective maintenance solution is needed. One such solution is opportunistic maintenance (OM). This work provides an overview of the maintenance strategy used within other industries before providing an in-depth review of the work specific to offshore wind. The existing literature fails to agree on the specific definition of the term. This work proposes an all-encompassing definition of the term, reviewing maintenance ‘opportunities’ and their corresponding ‘action/response’. The review found that maintenance opportunities are either internal or weather-based, with each opportunity having a pre-determined trigger/response. This work proposes the introduction of a market-based opportunity, which has not been previously considered. As offshore wind farms now face increasing curtailment and negative pricing threats, this new OM framework, OM+, view these periods as maintenance opportunities. OM+ also provides a new definition for recording and reporting availability — moving from time/energy-based availability to market-based availability.@en

The hydromechanics analysis of floating offshore wind turbines is a fundamental and time consuming part of the design process, traditionally analysed with methods of computational fluid dynamics. In this work, an alternative computational framework is suggested, able to significantly accelerate the design process with minimal accuracy loss. Through the use of a state-of-the-art potential-flow code, a surrogate model is developed with the aim to approximate the Response Amplitude Operators of any arbitrary floating offshore wind turbine of the spar buoy type. The results, measured in terms of accuracy and computational effort, demonstrate that this approach is able to approximate the potential-flow solver with very high accuracy at a fraction of the computational cost.@en

The hydromechanics analysis of floating offshore wind turbines is a fundamental and time consuming part of the design process, traditionally analysed with methods of computational fluid dynamics. In this work, an alternative computational framework is suggested, able to significantly accelerate the design process with minimal accuracy loss. Through the use of a state-of-the-art potential-flow code, a surrogate model is developed with the aim to approximate the Response Amplitude Operators of any arbitrary floating offshore wind turbine of the spar buoy type. The results, measured in terms of accuracy and computational effort, demonstrate that this approach is able to approximate the potential-flow solver with very high accuracy at a fraction of the computational cost.@en

The global installed capacity of floating offshore wind turbines is projected to increase by at least 100 times over the next decades. Station-keeping of floating offshore renewable energy devices is achieved through the use of mooring systems. Mooring systems are exposed to a variety of environmental and operational conditions that cause corrosion, abrasion, and fatigue. Regular physical in-service inspections of mooring systems are the golden standard for monitoring their health status. This approach is often expensive, inefficient, and unsafe, and for this reason, researchers are focusing on developing tools for digital solutions for real-time monitoring. Floating offshore renewable energy devices are usually equipped with a wide range of sensors, some low-cost, low/zero maintenance, and easily deployable (e.g., accelerometers on the tower), contrary to others (e.g., direct tension mooring line measurements), producing real-time data streams. In this paper, we propose exploiting the data coming from the first type of sensors for mooring systems health status nowcasting. In particular, we will first rely on state-of-the-art supervised shallow and deep learning models for predicting the health status of the different sections of the mooring lines. Then, since these supervised models require types and amount of data that are seldom available, we will propose new shallow and deep weekly supervised models that require a very small amount of data regarding worn mooring lines. Results will show that these last models can potentially have practical applicability and impact for real-time monitoring of mooring systems in the near future. In order to support our statements, we will make use of data generated with a state-of-the-art digital twin of the mooring system, OrcaFlex, for a floating offshore wind turbine reproducing the physical mechanism of the mooring degradation under different loads and environmental conditions. Results will show errors around 1% in the simplest scenario and errors around 4% in the most challenging one, confirming the potentiality of the proposed approaches.@en

The global installed capacity of floating offshore wind turbines is projected to increase by at least 100 times over the next decades. Station-keeping of floating offshore renewable energy devices is achieved through the use of mooring systems. Mooring systems are exposed to a variety of environmental and operational conditions that cause corrosion, abrasion, and fatigue. Regular physical in-service inspections of mooring systems are the golden standard for monitoring their health status. This approach is often expensive, inefficient, and unsafe, and for this reason, researchers are focusing on developing tools for digital solutions for real-time monitoring. Floating offshore renewable energy devices are usually equipped with a wide range of sensors, some low-cost, low/zero maintenance, and easily deployable (e.g., accelerometers on the tower), contrary to others (e.g., direct tension mooring line measurements), producing real-time data streams. In this paper, we propose exploiting the data coming from the first type of sensors for mooring systems health status nowcasting. In particular, we will first rely on state-of-the-art supervised shallow and deep learning models for predicting the health status of the different sections of the mooring lines. Then, since these supervised models require types and amount of data that are seldom available, we will propose new shallow and deep weekly supervised models that require a very small amount of data regarding worn mooring lines. Results will show that these last models can potentially have practical applicability and impact for real-time monitoring of mooring systems in the near future. In order to support our statements, we will make use of data generated with a state-of-the-art digital twin of the mooring system, OrcaFlex, for a floating offshore wind turbine reproducing the physical mechanism of the mooring degradation under different loads and environmental conditions. Results will show errors around 1% in the simplest scenario and errors around 4% in the most challenging one, confirming the potentiality of the proposed approaches.@en

Meeting climate and air quality targets, while preserving the focus on the reliability and cost-effectiveness of energy, became a central issue for offshore wind turbine engineers. Floating offshore wind turbines, which allow harnessing the large untapped wind resources in deep waters, are highly complex and coupled systems. Subsystem-level optimisations result in suboptimal designs, implying that an integrated design approach is important. Literature saw a few attempts on multidisciplinary design analysis and optimisation of floating wind turbines, with varying results, proving the need for an efficient, and sufficiently accurate, integrated approach. This paper reviews the state-of-the-art approaches to multidisciplinary design analysis and optimisation of floating support structures. The choice of the optimisation framework architecture, support platform design variables, constraints and objective functions are investigated. The techno-economic analysis models are closely examined, focusing on the approaches to achieving the optimum accuracy–efficiency balance. It is shown that the representation of the fully coupled system within the optimisation framework requires the introduction of a more complex multidisciplinary analysis workflow. Methods to increase the efficiency of such frameworks are indicated. Non-conventional support structure configurations can be conceived through the application of more advanced parametrisation schemes, which is feasible together with design space size reduction techniques. The set of design criteria should be extended by operation and maintenance cost, and power production metrics. The main technical limitations of the frameworks adopted so far include the inability to accurately analyse a diverse range of support structure topologies in multiple design load cases within a common framework. The cost approximation models should be extended by the chosen aspects of pre-operational phases, to better explore the benefits of the floating platforms.@en

Meeting climate and air quality targets, while preserving the focus on the reliability and cost-effectiveness of energy, became a central issue for offshore wind turbine engineers. Floating offshore wind turbines, which allow harnessing the large untapped wind resources in deep waters, are highly complex and coupled systems. Subsystem-level optimisations result in suboptimal designs, implying that an integrated design approach is important. Literature saw a few attempts on multidisciplinary design analysis and optimisation of floating wind turbines, with varying results, proving the need for an efficient, and sufficiently accurate, integrated approach. This paper reviews the state-of-the-art approaches to multidisciplinary design analysis and optimisation of floating support structures. The choice of the optimisation framework architecture, support platform design variables, constraints and objective functions are investigated. The techno-economic analysis models are closely examined, focusing on the approaches to achieving the optimum accuracy–efficiency balance. It is shown that the representation of the fully coupled system within the optimisation framework requires the introduction of a more complex multidisciplinary analysis workflow. Methods to increase the efficiency of such frameworks are indicated. Non-conventional support structure configurations can be conceived through the application of more advanced parametrisation schemes, which is feasible together with design space size reduction techniques. The set of design criteria should be extended by operation and maintenance cost, and power production metrics. The main technical limitations of the frameworks adopted so far include the inability to accurately analyse a diverse range of support structure topologies in multiple design load cases within a common framework. The cost approximation models should be extended by the chosen aspects of pre-operational phases, to better explore the benefits of the floating platforms.@en

As the installed capacity of individual turbines increases, so do costs associated with manufacture and maintenance. One proposed solution to this problem is the Multi-Rotor System (MRS) which utilises many small rotors to yield the same energy capture as a single large turbine. The operational advantage of the MRS is the built in redundancy between rotors on the same structure. However, despite this advantage, an increase in number of components is likely to result in an increase in transfers. This work examines the balance between additional crew and vessel requirements for such a structure against the expected savings in downtime due to redundancy and small rotor power rating. Three scenarios are analysed to determine the distribution of the failures which contribute to downtime. The study aims to find the optimal vessel fleet which limits downtime without drastically increasing direct operational expenditure (OpEx). As site size increases, the impact of global failures, which shut down the whole asset, is lessened. However, there is a significant increase in the number of vessels required to reduce downtime to <10% of the total OpEx. While a large fleet can offer significant downtime savings, there are practical limitations and challenges which must be acknowledged. @en

Floating Offshore Wind Turbines (FOWT) can harness the abundant wind resource in deep-water offshore conditions. However, they face challenges in harsh, unsheltered marine environments. The mean hydro- and aerodynamic loads coupled with fluctuating stochastic wind and wave loads contribute to varied failure mechanisms. Therefore, the serviceability, ultimate, and fatigue limit states are vital in ensuring the safety and reliability of FOWT. This paper investigates how specific loads and states drive the design of a spar-type support structure, utilising a computationally efficient frequency-domain model. This approach combines quasi-static aerodynamic and mooring models with a potential-theory-based radiation-diffraction solver. The serviceability criteria concern the platform and tower top displacements and accelerations. The ultimate and fatigue limit states are assessed for the tower base, the waterline section, and the mooring lines, including the effects of yielding under the bending moment and compressive axial load, column buckling, and tension-tension effects in the mooring lines. The full factorial design of experiments is employed to investigate the non-trivial relationships between the limit states and the various features of the support structure. The results demonstrate that the design of the spar platform above the waterline is mainly driven by fatigue, which results from significant dynamic tilt and increased stress concentration at the platform-tower intersection. On the other hand, the catenary mooring lines' design is mainly driven by the requirements of maximum offset (serviceability limit state) and fatigue.@en

A large deployment of energy storage solutions will be required by the stochastic and non-controllable nature of most renewable energy sources when planning for higher penetration of renewable electricity into the energy mix. Various solutions have been suggested for dealing with medium- and long-term energy storage. Hydrogen and ammonia are two of the most frequently discussed as they are both carbon-free fuels. In this paper, the authors analyse the energy and cost efficiency of hydrogen and ammonia-based pathways for the storage, transportation, and final use of excess electricity from an offshore wind farm. The problem is solved as a linear programming problem, simultaneously optimising the size of each problem unit and the respective time-dependent operational conditions. As a case study, we consider an offshore wind farm of 1.5 GW size located in a reference location North of Scotland. The energy efficiency and cost of the whole chain are evaluated and compared with competitive alternatives, namely, batteries and liquid hydrogen storage. The results show that hydrogen and ammonia storage can be part of the optimal solution. Moreover, their use for long-term energy storage can provide a significant, cost-effective contribution to an extensive penetration of renewable energy sources in national energy systems.@en

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.@en

The number of installed floating offshore wind turbines (FOWTs) has doubled since 2017, quadrupling the total installed capacity, and is expected to increase significantly over the next decade. Consequently, there is a growing consideration towards the main challenges for FOWT projects: monitoring the system’s integrity, extending the lifespan of the components, and maintaining FOWTs safely at scale. Effectively and efficiently addressing these challenges would unlock the wide-scale deployment of FOWTs. In this work, we focus on one of the most critical components of the FOWTs, the Mooring Lines (MoLs), which are responsible for fixing the structure to the seabed. The primary mechanical failure mechanisms in MoLs are extreme load and fatigue, both of which are functions of the axial tension. An effective solution to detect long-term drifts in the mechanical response of the MoLs is to develop a Digital Twin (DT) able to accurately predict the behaviour of the healthy system to compare with the actual one. Moreover, we will develop another DT able to accurately predict the near future axial tension as an effective tool to improve the lifespan of the MoLs and the safety of FOWT maintenance operations. In fact, by changing the FOWT operational settings, according to the DT prediction, operators can increase the lifespan of the MoLs by reducing the stress and, additionally, in the case where FOWT operational maintenance is in progress, the prediction from the DT can serve as early safety warning to operators. Authors will leverage operational data collected from the world’s first commercial floating-wind farm [the Hywind Pilot Park (https://www.equinor.com/en/what-we-do/floating-wind/hywind-scotland.html.)] in 2018, to investigate the effectiveness of DTs for the prediction of the MoL axial tension for the two scenarios depicted above. The DTs will be developed using state-of-the-art data-driven methods, and results based on real operational data will support our proposal.@en

New wind turbine technologies and designs are being explored in order to reduce the cost of energy from offshore wind farms. Two potential routes to a lower cost of energy are the X- Rotor Concept (XRC) and Multi-Rotor System (MRS) turbines. A key cost saving for both Novel concepts included in this paper is operation and maintenance (O&M) costs savings. The major component replacement cost for conventional horizontal axis, XRC and MRS turbines are examined and the benefits of the concepts are provided in this paper. A review on existing decision support systems for offshore wind farm O&M planning is presented with a focus on how applicable these previous models are to novel turbine concepts, along with analysis of how the influential factors can be modified to effectively model XRC and MRS.@en

The deployment of offshore wind in the UK has seen a rapid increase in the past decade and will continue to increase with the securement of the recent Scotwind sites. Floating platforms will be utilised for 60% of these new sites, creating opportunities to try new platform typologies and further solidify the validity of existing concepts. Since there is no consensus on the platform typology, the cost will vary; however, it is predicted to be double the price of traditional fixed platforms. Finding the most optimal solution in terms of cost and performance is key to keeping cost low, allowing the technology to be more competitive. A technique which has been used in other industries is multi-objective optimisation, searching a large design space much more quickly than traditional methods. By carrying out a multi-objective approach, the optimal platform geometry can be identified over the Pareto Frontier, considering conflicting objectives such as cost and performance. The aim of this work is to review the existing literature on multi-objective optimisation of floating offshore wind (FOW) platforms, highlighting the gaps and shortfalls in the current literature. This review highlights the majority of work has been carried out for the 5 MW NREL turbine on a SPAR platform, utilising a genetic algorithm. Cost reduction has been noted as the main objective, however, the models found within the literature are simplistic, with a number of assumptions. The overall findings of this work highlight future work that could be improved: cost models, the inclusion of an energy production model linked to the platform motion, the requirement for analysis of larger turbines and the potential for a concept selection tool to reduce computational time.@en

The development and deployment of offshore wind farms in the last decade have seen a dramatic increase, now totalling 743 GW globally (Global Wind Energy Council, 2022). This rapid increase is expected to further continue now with the potential to explore deeper sites with the adoption of floating offshore platforms. Proof of this growth has recently been seen with an impressive 60% of the 25 GW Scotwind leasing sites planning to install floating platforms in the next ten years (Crown estate, 2022 [1,2]). One main disadvantage of the advancement offshore is uncertainty and the potential increase in costs due to more complex structures and greater distances to shore. The cost increase for floating platforms is expected to be two to three times more expensive than traditional fixed support structures (Eric Paya, 2020). Thus, this work aims to review existing analytical cost models found within the literature to best determine their level of accuracy and compare the assumptions which have been made. Leading on from this review, a collection of all data found in the reviewed literature is presented, which leads to a data analysis that determines the variation across literature and the potential causes. Assessing this literature shows a wide range of model considerations, often leading to assumptions with little or no data to be validated against. Hence, high levels of variation and a lack of consensus on the cheapest floating platform were noted. All aspects of costs related to floating offshore wind systems vary heavily throughout the literature.@en

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.@en

The development of novel energy technologies is considered imperative in the provision of solutions to meet an increasing global demand for clean energy. Floating Offshore Wind Turbine (FOWT) is one of the emerging technologies to exploit the vast wind resources available in deeper waters. To lower the levelized cost of energy (LCOE) or optimise the performance response associated with a FOWT system, a detailed understanding of the different disciplines (Aero-Hydro-Servo-Elastic) within the system and the relationship between the FOWT system and the dynamics of the marine environment is required. This requires an efficient Multidisciplinary Design, Analysis and Optimisation (MDAO) framework for FOWT systems to reduce the capital cost and increase dynamic performance. A key component of any MDAO framework is the shape parameterisation scheme, as it enables the modelling of a large array of platform designs with different geometric shapes using limited number of parameters. This work focuses on the B-Spline parameterisation modelling technique of OC3 spar-buoy and the use pattern search optimization algorithm to select the optimal design variants. The parametrisation technique is implemented in an analysis framework, where a B-spline library from Sesam GeniE is used to model each design representation, and a potential flow frequency domain analysis solver (HydroD/Wadam) is used for the hydrodynamic analysis. Validation of the selected designs within the design space is conducted with a benchmark NREL5MW spar-buoy hydrodynamic response results in literature with the hydrodynamic response of the frequency domain modelling approach using Sesam GeniE and HydroD/Wadam. This analysis process shows a high accuracy in response results between the OC3 spar-buoy in literature and the OC3 spar-buoy model design using B-Spline parametrization technique. Key performance metrics like the cost of materials and root mean square (RMS) of the nacelle acceleration also show improvement with the design variants compared to estimation from OC3 design in literature. @en

As the UK takes a step towards a greener, cleaner future aiming to be net zero by 2050, continuous development of the power network is required. A clear solution is offshore wind, having already proved its feasibility and success in nearshore sites. However, a large majority of near shore sites in the UK are already being utilised. The next step is to move into deeper waters and utilise the stronger, more consistent wind resources. A solution could be floating offshore wind which is still in its infancy, with only a few operational floating wind farms installed. Building upon the multidisciplinary design, analysis, and optimisation framework (MDAO) for floating offshore wind turbines (FOWT) being developed at the University of Strathclyde, called FEDORA, the aim of this work is to refine the LCoE model adopted by FEDORA, and applying it to perform the optimisation of the floating offshore OC3 SPAR. There is limited data on cost, therefore Hywind Scotland Pilot Park will be used as a basis for the LCoE model, allowing the results to be validated. This model is not restricted to SPARs, as it establishes a general methodology to calculate the life cycle cost of floating offshore wind farms. Utilising the improved cost model this work finds four optimised SPAR structures for four different maximum angles of inclination which can be experienced in the wind turbines operation. The improved cost model has a much higher accuracy, highlighting the initial cost model underestimates the cost of the SPAR structure by around half. @en