Coupled Aero–Hydrodynamic Analysis in Floating Offshore Wind Turbines: A Review of Numerical and Experimental Methodologies

Abstract

Floating offshore wind turbines (FOWTs) have received increasing attention as a crucial component in renewable energy systems in recent years. However, due to the intricate interactions between aerodynamics and hydrodynamics, accurately predicting the performance and response remains a challenging task. This study examines recent advancements in the coupled aero–hydrodynamic numerical simulations for horizontal-axis FOWTs, categorizing existing research by coupling methods: uncoupled, partially coupled, and fully coupled. The review summarizes models, methodologies, and key parameters investigated. Most partially coupled analyses rely on forced oscillation, while the interplay between aerodynamics and elasticity, as well as interactions among multiple FOWTs, remain under-explored. Additionally, this review describes relevant physical model tests, including wave basin tests, wind tunnel tests, and real-time hybrid tests (RTHT). Although RTHT faces issues related to system time delays, they have garnered significant attention for addressing scale effects. The paper compares the three coupling methods, emphasizing the importance of selecting the appropriate approach based on specific design stage requirements to balance accuracy and computational efficiency. Finally, it suggests future research directions, offering a meaningful reference for researchers engaged in studying the aero–hydrodynamic behavior of FOWTs.

1. Introduction

In recent decades, the utilization of renewable energy sources has become increasingly important due to global climate change and heightened energy awareness. Major global economies, including China, the European Union, the United States, Japan, and others, have successively set carbon neutrality goals and have identified offshore wind energy as a crucial means to achieve carbon neutrality. Offshore wind energy offers numerous advantages, such as abundant wind resources, high wind speeds, low turbulence intensity, fewer spatial constraints, and relatively minimal visual and environmental impact [1,2]. According to the 2024 Global Wind Report, the global wind power capacity increased by a record 117 GW in 2023, marking the best year in history, with 11 GW coming from offshore wind energy. Meanwhile, the Global Wind Energy Council (GWEC) predicts that over the next decade (2024–2033), the global offshore wind capacity will increase by an additional 410 GW [3].

Offshore wind turbines can be categorized into two main types: fixed-bottom wind turbines and floating wind turbines. Fixed-bottom ones are primarily situated in coastal areas with water depths of less than 60 m. However, approximately 80% of the global offshore wind energy resources are located in areas with water depths exceeding 60 m [4]. To tackle this challenge, Heronemus [5] proposed the notion of the floating offshore wind turbine (FOWT).

FOWTs primarily consist of four main components: the turbine (including blades and a hub), the tower, the floating support platform, and the station-keeping systems (including moorings and tendons). They can be divided into two categories: horizontal-axis wind turbines (HAWTs) and vertical-axis wind turbines (VAWTs), depending on their axis of rotation. In HAWTs, the blades revolve around a horizontal axis aligned with the wind direction, while in VAWTs, the rotation occurs around a vertical axis perpendicular to the wind direction, as illustrated in Figure 1a. HAWTs require a yaw control mechanism to orient the rotor with respect to wind direction, whereas VAWTs can harness wind from any direction. The advancements in VAWTs were reviewed by Zhao et al. [6]. The global status of the commissioned HAWTs is listed in

Table 1. Global status of the commissioned FOWTs from 2009 to 2024.

Platform	Location	Project Name	Details of Wind Turbines	Year
Semisubmersible	Portugal	WindFloat Atlantic Phase 1	2 MW	2011
		WindFloat Atlantic Phase 2	3 × 8.3 MW	2019
	Japan	Fukushima Forward Phase 1	2 MW	2013
		Fukushima Forward Phase 2	7 MW	2015
		IDEOL Kitakyushu Demo	3 MW	2018
	France	Floatgen	2 MW	2018
		Eolink	3 × 5 MW	2024
	UK	Kincardine Phase 1	2 MW	2018
		Kincardine Phase 2	5 × 9.5 MW	2021
	Spain	W2Power	2 MW	2019
	Republic of Korea	Ulsan Demo	0.75 MW	2020
	China	Three Gorges Leading	5.5 MW	2021
		Fuyao	6.2 MW	2022
		Guanlan	7.25 MW	2023
	Ireland	AFLOWT	6 MW	2022
	Norway	Flagship	11 MW	2024
Spar	Norway	Hywind	2.3 MW	2009
		Tetraspar Demonstration	3.6 MW	2021
		Hywind Tampen	11 × 8.6 MW	2023
	Japan	Kabashima	2 MW	2013
		Fukushima Forward Phase 3	5 MW	2016
		Sakiyama	2 MW	2016
	UK	Hywind Pilot Plant	6 MW	2017
Tension Leg Platform	Spain	Pivot Buoy	0.22 MW	2021
	France	Provence Grand Large	3 × 8.4 MW	2024
Barge	Spain	DemoSATH	2 MW	2023

. Floating platforms can be classified into buoyancy-stabilized types (such as barge and semisubmersible), mooring-stabilized types (tension leg), and ballast-stabilized types (spar), according to their respective roles in providing restoring moments for roll and pitch, as illustrated in Figure 1b. The majority of the installed HAWTs employ semisubmersible platforms (see Table 1), which are the focal point of this paper.

Due to the intricate nature of ocean environments, FOWTs are subjected to a huge variety of environmental load conditions, such as wind, ice, salt fog, and rain, while floating support platforms have to withstand hydrodynamic forces, structural stresses, and the loads from mooring systems. These loads often do not act independently but exhibit certain coupling relationships. The interaction between wind turbines and floating support platforms can lead to significant differences in aerodynamic performances when compared to fixed wind turbines. For example, Figure 2 depicts the changes in the flow field around the blades caused by the pitching motion of the platform. The blue arrow represents the direction of the wind force. Under normal working state (NWS), the airflow through the blades forms a uniform wake, keeping the platform relatively stable with minimal pitch activity. However, as the pitch motion intensifies, the turbine may transition into a turbulent wake state (TWS) or a vortex ring state (VRS). When the pitch angle reaches its limit, the blades nearly face against the wind, entering the windmill braking state (WBS). In this state, the wake will reverse, leading to a significant reduction in aerodynamic efficiency and causing structural damage to the turbine. Consequently, analyzing the fully coupled response of FOWTs has consistently been an important issue and remains a challenging problem.

Experimental projects on FOWT modeling have provided the research community with detailed data, establishing reference models that offer valuable accuracy for future research. Most published studies that conducted numerical simulations utilized NREL 5 MW (National Renewable Energy Laboratory) [7], DTU 10 MW (Danmarks Tekniske Universitet) [8], and IEA 15 MW (International Energy Agency) [9] wind turbine models. In particular, the NREL 5 MW model has been the most extensively studied in analyzing the aerodynamic performances of FOWTs. Data from the IEA 22 MW [10] model were publicly released in April 2024. In the field of hydrodynamics, the semisubmersible platforms in OC4 [11] and OC5 [12] from the U.S. DeepCwind project are mostly studied due to their comprehensive and high-quality test data. The main difference between them in terms of hydrodynamics is that OC4 focused on the characteristics of the platform, whereas OC5 adjusted the mass density and extensional stiffness of the mooring system, consequently reducing the motion response of the platform [13]. It is important to note that there are two other typical types: OO-Star [14] and VolturnUS-S [15]. The OO-Star design removes the bracing components in the middle, featuring a cylindrical shape above the waterline and a conical shape below. The VolturnUS-S comprises four columns and three radial components. The OO-Star platform primarily uses concrete as its main material, while other platforms are constructed with steel structures. However, there are fewer studies available on this aspect, so further research is needed regarding simulations in this area.

Up to now, three methods have been employed for dynamic studies on FOWTs: on-site measurement, physical model test, and numerical simulation [16]. On-site measurement is challenging to implement widely due to its high cost and the exclusive data rights by various companies and research institutes. The physical model test is used to validate the numerical simulation, and the numerical simulation is used to predict FOWTs’ responses. This review prioritizes articles published in leading journals such as Energy, Renewable Energy, Ocean Engineering, Journal of Physics: Conference Series, Journal of Marine Science and Engineering, and Energies, focusing on studies that utilize CFD, FAST (OpenFAST), and ANSYS-AQWA. The main objective of this paper is to review and analyze the progress of research in the aero–hydrodynamic coupled numerical simulations on HAWTs over the past decade. By reviewing the models, methodologies, and key parameters of different coupling methods (uncoupled, partially coupled, and fully coupled), this paper summarizes their characteristics, applicability, and the limitations of current research. Furthermore, this work also provides related physical model tests, including wind tunnel tests, wave basin tests, and real-time hybrid tests (RTHT). Depending on the difference between the virtual and physical components, RTHT can be classified into RTHT based on numerical wind tunnel and RTHT based on numerical wave basin. Finally, this paper summarizes the three coupling methods and the associated physical model tests and proposes future research directions. The paper aims to provide researchers with a comprehensive reference on the aero–hydrodynamic behavior of FOWTs in order to advance the development of this field.

2. Numerical Simulation Methods

Since FOWTs are exposed to both wind and wave loads, it is crucial to assess the hydrodynamic and aerodynamic loads influencing FOWTs for a comprehensive understanding of their global motions. The different approaches employed to evaluate these loads are described in this section.

2.1. Aerodynamics

Blade element momentum (BEM) theory is one of the earliest and most widely used methods for calculating the aerodynamic performances of wind turbines. Due to its simplicity and rapid computation, BEM has been applied in modern engineering tools for wind turbine design and simulation, such as OpenFAST (formerly FAST), Q-Blade, and HAWC2. These software are considered to have mid-fidelity [4,17] and are often regarded as engineering tools. To the authors’ knowledge of the available literature in the past decade, FAST is the most widely used, and it is mainly focused on in this paper. Glauert [18] combined blade element theory with momentum theory. The former assumes that a blade can be divided into infinitely small elements that act independently from each other. The thrust and torque exerted on the entire blade are calculated by summing aerodynamic forces on these small elements, allowing the power output of the turbine to be determined. The thrust and torque distributed around an annulus of width dr are equivalent to the following:

$$\mathrm{d}T={\displaystyle \frac{1}{2}}n\rho W^2(C_{\mathrm{l}}\cos \phi +C_{\mathrm{d}}\sin \phi )c\mathrm{d}r$$

(1)

$$\mathrm{d}M={\displaystyle \frac{1}{2}}n\rho W^2(C_{\mathrm{l}}\sin \phi -C_{\mathrm{d}}\cos \phi )cr\mathrm{d}r$$

(2)

where n represents the number of blades, C_l is the lift coefficient, C_d is the drag coefficient, ϕ is the local flow angle, r is the local radius, c is the chord length, and W stands for the total velocity seen by the blade element. Conversely, the momentum theory assumes that the pressure or momentum loss of a wind turbine is a result of the work performed by the airflow on blade elements. Consequently, the thrust and torque acting on the blades can be determined by analyzing the pressure or momentum loss. The thrust and torque are as follows:

$$\mathrm{d}T=4\mathsf{\pi }\rho Ua(1-a)\mathrm{d}r$$

(3)

$$\mathrm{d}M=4\mathsf{\pi }{r}^3\rho U\Omega (1-a)b\mathrm{d}r$$

(4)

where U represents the mean wind speed, a is the axial induction factor, b is the rotational or tangential induction factor, and Ω stands for the rotor rotational speed. Since the thrust and torque of a wind turbine, solved by the two methods, are equal, iterative calculations can be used to calculate thrust, torque, and power output.

Due to its idealized assumptions, the predictions of BEM theory on aerodynamic performances are not sufficiently accurate. Therefore, researchers have introduced various corrections on specific phenomena to refine the results, including the corrections for blade tip loss, induced velocity, dynamic stall effects, and wake effects. Nevertheless, these modified models still face challenges in achieving the required accuracy, especially under complex operating conditions. The theory of the generalized dynamic wake (GDW) is grounded in the potential flow solution of the Laplace equation, addressing inviscid and incompressible gas flow. Peters et al. [19] initially introduced GDW to scrutinize the aerodynamics of helicopter rotors, and subsequently, Suzuki [20] refined it for the investigation of wind turbine aerodynamics. In contrast to BEM, GDW focuses on more accurately modeling the wake interactions between wind turbine blades, taking into account the non-uniform distribution and dynamic evolution of wakes, thereby providing more precise predictions. However, this theory exhibits significant deviations in situations characterized by low wind speeds, blade deformation, and substantial impeller cone angles.

The free vortex method (FVM) [21] is an approach for analyzing the aerodynamic performance of FOWTs, which is closer to physical reality compared to the BEM, with higher computational accuracy and adaptability. This method assumes the flow field to be inviscid and incompressible and consists primarily of two components: the blade element model and the free vortex wake model. The blade element model is one of the core components of FVM, which models the blades to describe their aerodynamic characteristics. Based on the complexity of the model, it can be divided into three categories: panel model [22], lifting surface model [23], and lifting line model [24]. The free vortex wake model describes the vortex structure in the wake behind the wind turbine. In FVM, it is assumed that each wind turbine blade generates vortices during motion, which are carried downstream by the airflow to form the wake. By simulating the evolution of these vortices, the aerodynamic performance of FOWTs can be more accurately reflected.

Computational fluid dynamics (CFD) involves the direct solution of the Navier–Stokes (N-S) equations, which articulate the preservation of momentum for fluids that are both viscous and incompressible. It provides various physical properties within a wind turbine system, including velocity, pressure, thrust, power, and so on. Furthermore, CFD allows for the analysis of the time-dependent behavior of these physical properties, as well as the study of vortices and wake effects. The choice of turbulence model is critical in CFD simulations, and for FOWTs, the available models are detached eddy simulation (DES), Reynolds averaged Navier–Stokes (RANS), and large eddy simulation (LES). CFD is highly accurate but can be time-consuming due to extensive calculations. To simplify modelling for the complex system of FOWTs, two approaches are established in CFD simulations: the actuator disc model (ADM) [25] and the actuator line model (ALM) [26]. In ADM, the rotor’s impact on the flow field is considered by substituting the blades with volume forces, treated as source terms in the N-S equation. Conversely, in ALM, a similar effect is incorporated by employing tabulated airfoil data to depict the loading on each blade. The iterative process involves computing local angles of attack based on the blade movement and the local flow field to determine airfoil data and loads. Due to the dependency of the actuator model on airfoil data, there are inherent inaccuracies in such simulations. The next generation of the actuator model can be implemented using the geometry-resolved CFD method to achieve greater precision [27,28]. Future developments will focus on striking a balance between computational cost and accuracy.

At present, high-fidelity commercial tools, such as OpenFOAM, STAR-CCM+, and FLUENT, all rely on CFD. With the continuous development in computer technology and calculation methods, CFD shows a growing significance in the analysis of the aerodynamic behavior of wind turbines. Among all the methods mentioned above, the CFD method is considered a high-fidelity model for solving aerodynamic performance.

2.2. Hydrodynamics

The dynamic forces on floating platforms for FOWTs arise from the combined effects of waves, wind, currents, and the counteracting force exerted by the mooring system. Consequently, the aerodynamic performances of the blades in FOWTs are greatly affected by the response of floating platforms to these dynamic loads, highlighting the importance of accurate predictions. Because the floating foundation of FOWTs is largely derived from standard offshore floating platforms, theoretical approaches suitable for the latter can also be utilized. These methods primarily include the Morison equation (ME), potential flow (PF) theory, and CFD.

When the characteristic dimension (D) of the platform is significantly smaller than the wavelength (L) of waves, the hydrodynamic loads on a floating platform can be determined using ME, which is a semi-empirical formula introduced by Morison et al. [29], that serves as a valuable tool in such calculations and is expressed using the following equation:

$$F=C_{\mathrm{m}}\rho {\displaystyle \frac{\pi D^2}{4}}{\displaystyle \frac{\partial u}{\partial t}}+C_{\mathrm{d}}{\displaystyle \frac{\rho }{2}}Du\left|u\right|$$

(5)

where D is the diameter of the floating platform, F represents the hydrodynamic force, u is the velocity of water, C_m is the inertia force coefficient, and C_d stands for the drag coefficient. ME assumes that wave motions are not impacted by a floating platform’s existence because of its small dimension, and the effect of waves on a floating platform is mainly due to the viscous effect and added mass effect. ME is still widely used for calculating wave forces on slender cylindrical bodies (D/L < 0.2). It has certain limitations when applied to larger-scale cylinders, high-frequency waves, and more complex hydrodynamic conditions, where appropriate corrections need to be added.

When floating platforms are significantly large (D/L > 0.2), hydrodynamic loads are typically evaluated using PF theory. It accounts for the influence of a floating platform on wave motions due to its large size. Since the inertia force is greater than the viscous force, the viscous effect is not taken into account. In PF theory, first-order wave forces and second-order wave forces describe two aspects of the influence of waves on fluid motion. First-order wave forces describe the linear effects of waves, representing the direct impact of waves and constituting the primary component of total wave forces. In contrast to first-order wave forces, second-order wave forces describe the nonlinear effects of waves, considering wave deformation and nonlinear effects. Overall, first-order wave forces and second-order wave forces are important concepts in potential flow theory for describing the influence of waves on fluid motion. Owing to its straightforward approach and excellent efficiency, PF is presently an extensively utilized technique for determining hydrodynamic loads on floating platforms. Several commonly used commercial tools, such as SESAM and ANSYS-AQWA, are also based on PF theory. In this paper, we focus primarily on the results of ANSYS-AQWA, which is mostly employed in analyzing the performances of FOWTs.

Since the geometry of floating platforms is complex, a combination of the PF and ME methods is often employed in numerical simulations. This hybrid approach utilizes PF to calculate the behavior of large-scale components and employs ME to determine the response of smaller-scale components. This combination significantly improves accuracy while maintaining low computational costs, meeting the requirements for engineering applications.

CFD method is employed to accurately determine hydrodynamic loads on a floating platform, incorporating an accurate representation of free surface effects using the volume of fluid (VOF) method. CFD provides precise modeling of viscous, diffraction, radiation effects, and more. However, it demands more computational effort and time. Given that these findings are useful in determining the nonlinear wave’s degree of effect, CFD seems to be the only choice in some cases, and it has been adopted in recent studies.

Among all the methods mentioned above, the ME and PF hybrid methods and CFD methods are considered high-fidelity models for solving hydrodynamic performance [4].

3. Uncoupled Analysis

The operating performances of FOWTs are influenced by both wind and waves. In an uncoupled analysis, the dynamic performances of FOWTs are evaluated by considering one factor at a time (either wind or waves). In aerodynamic analyses, numerical simulations are used to assess the performances of a turbine under varying wind conditions, excluding considerations of the influence of the floating platform and mooring system. In hydrodynamic analysis, the performances of a floating platform are simulated under different wave conditions without accounting for the effect of the upper turbine. The research advances in uncoupled numerical simulations for FOWTs are discussed in this section.

3.1. Aerodynamics Analysis

Through uncoupled numerical simulations, several key issues are addressed, including the influence of blade rotation on thrust and power, the impact of the tower on the blades, blade wake and flow characteristics, and aeroelasticity.

3.1.1. Power Output

The power output of FOWTs is inherently dependent on the temporal and spatial distribution of wind resources and the efficiency of wind energy utilization. Therefore, the studies on steady wind, wind shear, and turbulent wind in the atmospheric boundary layer, which represent the inflow conditions for FOWTs, are of significant importance. Wind shear refers to the change in wind direction or speed in horizontal or vertical direction. Due to the huge geometry of FOWTs, the hub heights can reach over a hundred meters above sea level, making the vertical gradient of wind speed notable. Therefore, the effects of vertical wind shear must be included. The studies on turbulent wind primarily focus on the time-averaged and transient characteristics of the wind velocity, as these directly influence the power output of FOWTs and the simulation accuracy.

Numerous studies have investigated the aerodynamic performances of wind turbines through uncoupled numerical simulations with steady wind inflow. Early advancements in this field were reviewed by Bai et al. [30]. In recent years, Chavan et al. [31] discovered that the blades near the sea surface experience lower wind speeds compared to the top blades due to the effect of vertical wind shear. Rotating wind turbines experience periodic loads, impacting their operating performances. To reduce power fluctuations in wind turbines under turbulent wind conditions, Xu et al. [32] developed a trailing-edge flap control strategy for an NREL 5 MW offshore floating wind turbine. Similarly, to optimize the power coefficient of the wind turbine, Wisatesajja et al. [33] analyzed the influence of tilt angles, ranging from under 3.5 deg to 6.1 deg, and found that the optimal power coefficient range corresponds to a tilt angle of 3.9 deg to 5.8 deg. Zhang et al. [34] studied a twin-rotor wind turbine under different wind directions. The results showed that when the twin-rotor system was arranged in a tandem configuration, the average output power of the system decreased by 44%. The output power of the system was highest when the wind direction angle was 45 deg.

3.1.2. Aeroelasticity

Since the blades are slender and elongated, the significance of the blade aeroelasticity cannot be ignored. Sayed et al. [35] performed fluid–structure interaction simulations for DTU 10 MW wind turbine using CFD-CSD (Computational Structural Dynamics). The findings suggested that the wind turbine’s power and thrust experienced an approximately 1% and 0.3% increase, respectively, attributable to rotor elasticity. Taking into account the impacts of tower shadow, wind shear, and yawed inflow, Li et al. [36] conducted simulations using an aeroelastic coupling model for an NREL 5 MW wind turbine. The results indicated that the yaw moment increases significantly with wind speed while the power and thrust remain relatively stable. Yawed inflow led to a reduction in axial velocity, resulting in a significant decrease in power output and thrust. There are also many studies related to blade aeroelasticity [37,38,39]. CFD simulations highlight the advantages of direct modeling full-scale wind turbines. However, the computational demands for simulating full-scale models are very high. Therefore, scale models are utilized in some studies. Giahi et al. [40] conducted numerical simulations for a scaled-down (1:6) version of a 2 MW wind turbine, and the analysis results were compared with the predictions of similarity theory. The results revealed that the simulation outcomes perfectly match the anticipated values from the similarity theory. Pinto et al. [41] compared the aerodynamic performance of a scaled-down model (1:50) with the full-scale wind turbine, revealing that the simulations with the full-scale model align with previous numerical simulations, and the results based on the scaled-down model closely match the experimental data.

3.1.3. TSR (Tip Speed Ratio)

One crucial factor for assessing the aerodynamic performance of wind turbines is the TSR, which is the ratio of linear rotational speed to wind speed. When blades are longer or rotate faster, the TSR increases at the same wind speed. Kim et al. [42] conducted a numerical study on the impact of tower shadow on the blade thrust and fatigue loads at different TSRs. At lower wind speeds, the blade–tower interaction causes an increase in blade fatigue loads, but this effect diminishes at higher wind speeds. Tran et al. [43] developed a user-defined function (UDF) to incorporate extreme operating gust (EOG) conditions into FLUENT, significantly affecting the loads on turbine blades. Oggiano [44] conducted CFD simulations on NTNU (Norwegian University of Science and Technology) wind turbine at three different TSRs (3, 6, 10). They compared the loads, power, and thrust coefficients of the blades with experimental results, finding discrepancies in thrust and torque at a TSR of 10. Under the same TSRs, Kono et al. [45] employed improved delayed detached eddy simulations (IDDES) to investigate the effect of blade–tower interaction. They observed that the airflow induced by blade rotation significantly affects the aerodynamic loads on the tower. Sivalingam et al. [46] used CFX to conduct a similar study on an NREL 5 MW wind turbine with TSRs ranging from 3 to 9.5. The CFD results show similar trends to the experimental results, but the simulated amplitude of the thrust is higher than that of the experiments.

3.1.4. Other Aspects

Additional research focuses on assessing the accuracy of the RANs model. For example, Popescu et al. [47] performed numerical simulations on the NREL Phase VI to indicate that the model approach is reliable and efficient. Furthermore, Zhong et al. [48] used a biplane airfoil instead of NREL Phase VI to design biplane blades. Compared to the baseline blade, the thrust and power of the biplane blades were significantly increased. Research on multiple wind turbines has also received attention. Zhang et al. [49] investigated the equivalent fatigue damage of upstream and downstream turbines by wake control and showed that the fatigue damage of downstream turbine increases by 50% as the yaw angle increases.

3.2. Hydrodynamics Analysis

A hydrodynamic analysis focuses on the behavior of floating platforms in a basin influenced solely by environmental waves. Numerical simulations are mostly performed considering regular or irregular wave conditions.

3.2.1. DeepCwind Semisubmersible Platforms

Benitz et al. [50] neglected the blade rotation and conducted CFD simulations of OC4 DeepCwind semisubmersible under regular waves using OpenFOAM to determine the corresponding hydrodynamic load. Furthermore, Tran et al. [51] performed CFD simulations encompassing an analysis of free-decay and regular wave on OC4 DeepCwind semisubmersible. Kim et al. [52] utilized ANSYS-AQWA for a numerical investigation of the OC4 DeepCwind semisubmersible subjected to irregular waves. Upon analysis, a noteworthy agreement was observed in the results of the response amplitude operator (RAO) compared to those derived from FAST. The results showed good agreement between the two tools. Wang et al. [53] conducted validation of the OC4 platform using CFD methods under bichromatic and irregular wave conditions. The results indicated that although the low-frequency response was under-predicted, the CFD results were more consistent with experimental values than those from mid-fidelity tools. Zeng et al. [54] numerically analyzed the OC4 DeepCwind semisubmersible subjected to freak waves featuring substantial crests and deep troughs. The platform motions were greatly intensified due to freak waves, and the impact of nonlinearities was more significant on large crests than on deep troughs.

For OC5 DeepCwind semisubmersible, Bruinsma et al. [55] conducted a similar analysis using a nonlinear numerical wave basin. They validated the results against the existing literature and simulated the intricate fluid–structure interaction between the mooring system and the platform structure. Burmester et al. [56] conducted an analysis of surge motion decay on OC5 DeepCwind semisubmersible. Wisudawan et al. [57] studied the dynamic response of scaled platforms with different Froude numbers under wave loads. The results indicated that the Froude number scaling is sufficiently consistent for both frequency and time-domain motion responses. Wang et al. [58] employed a CFD-FEM coupled code to compute the surge, pitch, and heave RAOs at wave frequency and double wave frequency under regular waves. They also studied mean surge offset and zero-frequency surge QTF of the semisubmersible platform.

Wang et al. [59] developed several modifications to the HydroDyn module in OpenFAST, enhancing its predictive capabilities for lateral and axial drag forces in low-frequency responses. Wang et al. [60] carried out a coordinated investigation involving CFD simulations and wave-basin experiments on OC6 DeepCwind semisubmersible. They focused on reducing the nonlinear low-frequency response and addressing resonance issues associated with surge and pitch motions at low frequencies.

3.2.2. Other Semisubmersible Platforms

Meng et al. [61] conducted a time domain analysis of the VolturnUS-S platform using ANSYS-AQWA, studying the influence of mooring line materials on platform dynamic response. The results indicated that synthetic fibers can replace steel wires as mooring materials. Saleh et al. [62] also investigated the fatigue loads of mooring systems made from nylon and polyester ropes under extreme environmental conditions. The results showed that nylon outperformed polyester. Zhong et al. [63] explored the hydrodynamic behavior of the VolturnUS-S semisubmersible platform under freak waves. It can be found that when COG (Central of Gravity) is moved to the centroid of the waterplane of the platform’s horizontal geometry, the displacement of surge motion and mooring tension can be reduced. Liu et al. [64] suggested an enhancement to the original three offset columns of the DeepCwind prototype by introducing a semisubmersible floating platform design with either uniformly distributed four or six offset columns. The results indicated a reduction in surge motion response with an increase in the number of offset columns. Johannesen et al. [65] studied the effect of heave plates on vibration damping for semisubmersible platforms. The results indicated that CFD tools can effectively simulate the added mass effects of heave plates under different conditions, achieving vibration reduction. Califano et al. [66] conducted CFD validation for the INO WINDMOOR semisubmersible platform support 12 MW wind turbine. The pitch and heave motion responses were found to be in good agreement with the experimental results.

3.3. Summary

The uncoupling analyses concentrate on either the aerodynamics of a wind turbine solely under wind conditions or the hydrodynamic performance of a floating platform exclusively under wave conditions. The complex interaction between them is neglected. This provides a foundation for optimizing blades or designing platforms, subsequently reducing computational costs for the coupled simulation of FOWT systems.

4. Partially Coupled Analysis

A partially coupled analysis simultaneously considers the influence of both wind and wave conditions on the dynamic performances of FOWTs. For instance, to study the aerodynamic performance of a wind turbine, the six degrees of freedom (DOF) motions of a platform are converted into sinusoidal or cosine functions, and a forced oscillation is applied to subject the wind turbine to this motion. To investigate the hydrodynamic performance of a platform, wind loads are simplified into forces and moments and then applied to the rotating center of the semisubmersible platform. This approach helps to reduce the complexity of the problem to a certain extent. The research advancements in partially coupled analyses for FOWTs are summarized in this section.

4.1. Aerodynamics Analysis

The motion of a platform is controlled by the amplitude and frequency in a partially coupled analysis, and the amplitude and frequency of these variations can be controlled through prescribed values.

Table 2. Summary of the partially coupled analysis in the aerodynamics of FOWTs.

Wind Turbine and Wind Speed (WS)	Platform Motion	Tool/Method	Aim/Conclusion/Focus of Study	Ref.
NREL 5 MW WS = 11 m/s	Pitch (Amplitude 4°; Frequency 0.1 Hz)	STAR-CCM+/CFD	Comparison of CFD simulation results with previous studies	Tran et al. [68] &
	Surge (Amplitude 4–16 m; Frequency 0.127–0.770 rad/s)		Variations in wake intensity under surge motion of the platform and blade–wake interaction.	Tran et al. [69] &
	Pitch and Yaw (Amplitude 1°and 4°; Frequency 0.1 Hz)		Effect of pitch and yaw motion on thrust and power at the same amplitude and frequency.	Tran et al. [70] &
	Surge (Amplitude 4–16 m; Frequency 0.127–0.770 rad/s)		Influence of platform motion and tower interference. Impact of blade–wake interaction.	Tran et al. [71] &
NREL 5 MW WS = 11.4 m/s	Surge (Amplitude 1.02 m; Period 9 s)	FLUENT/CFD	Comparison of BEM, GDW, and ADM methods at different TSRs.	Micallef et al. [72] --
NREL 5 MW WS = 8 m/s	Pitch (Amplitude 1.29°; Period 10 s)	FLUENT/CFD	Comparison between CFD and BEM methods, accurate capture of wind turbine wake rotation by CFD.	Sivalingam et al. [73] &
NREL 5 MW WS = 11.4 m/s and 13.2 m/s	Surge (Amplitude 0.0238–0.0952 m; Frequency 0.18 Hz–0.89 Hz)		Effect of surge motion on thrust and power. Blade wake under high amplitude and high frequency.	Sivalingam et al. [74] &
NREL 5 MW WS = 8 m/s and 11.2 m/s	Pitch (Amplitude 0.85°; Period 10 s)	OpenFOAM/CFD	Differences in results between CFD and BEM under large platform motions.	Wu et al. [75] &
NREL Phase VI WS = 7 m/s DTU 10 MW WS = 11 m/s	Pitch (Amplitude 3°and 5°; Period 8.8s) Yaw (Amplitude 3°; Period 8.8 s)	HMB3/CFD	Influence of different pitch amplitudes on blade vortices and wake.	Leble et al. [76] &
NREL 5 MW WS = 11.4 m/s	Surge (Amplitude 0–2.5 m; Frequency 0–3 Hz)	FVM	Influence of different tip speeds on thrust and power.	Wen et al. [77] --
DTU 10 MW WS = 4 m/s	Surge (Amplitude 0.008–0.035 m; Frequency 1–2 Hz)	Finite-Volume Flow solver/FVM and BEM	Influence of the amplitude and frequency of surge motion on thrust and power.	Cormier et al. [78] &
NREL 5 MW WS = 11.4 m/s	Surge (Amplitude 2 m; Period 12 s)	OpenFOAM/ CFD	Impact of blade flexibility on output power.	Liu et al. [79] --
NREL 5 MW WS = 11.4 m/s	Surge (Amplitude 8 m and 16 m; Frequency 0.1 Hz)	ALM and FVM	Limited predictive capability of ALM and FVM methods for dynamic stall phenomena.	Corniglion et al. [80] --
NREL 5 MW WS = 11.4 m/s	Pitch (Frequency 0.1 Hz; Amplitude 4° and 8°) Surge (Frequency 0.1 Hz; Amplitude 4° and 8°)	ELSA Software/CFD	Effects of surge motion and pitch motion on wind turbines are similar.	Lienard et al. [81] &
NREL 5 MW (1:50) WS = 1.61 m/s	Pitch (Amplitude 1.5°–3°; Period 0.375 s–3 s)	STAR-CCM+/CFD	Influence of the frequency and amplitude of surge motion on thrust and power.	Fang et al. [82] *
NREL 5 MW WS = 11.4 m/s	Pitch (Frequency 0.1–0.2 Hz; Amplitude 1°–4°) Surge (Frequency 0.1–0.2 Hz; Amplitude 1–4 m) Coupled surge and pitch motions	STAR-CCM+/CFD	Pitch motion has a greater impact on power than surge motion. Impact of the coupled pitch and surge motion on performances.	Feng et al. [83] &
NREL 5 MW WS = 11.4 m/s	Pitch (Frequency 0.1 and 0.2 Hz; Amplitude 2° and 4°;) Surge (Frequency 0.1 and 0.2 Hz; Amplitude 1 and 2 m;) Coupled surge and pitch motions	--	Comparison of power and thrust fluctuations when the pitch and surge motions are coupled at different frequencies and compared to coupling at the same frequency.	Guo et al. [84] &
NREL 5 MW WS = 11.4 m/s	Pitch (Amplitude 4° Frequency 0.1 and 0.2 Hz) Yaw (Amplitude 4°; Frequency 0.1 and 0.2 Hz) Coupled yaw and pitch motions	FLUENT/CFD	Effect of the frequency and amplitude of platform motions on thrust and power, with greater impact of pitch motion than yaw motion. Effect of the coupled motion of pitch and yaw on thrust and power	Chen et al. [85] &
	Pitch (Frequency 0.1 and 0.2 Hz; Amplitude 1°–4°;) Surge (Frequency 0.1 and 0.2 Hz; Amplitude 1 and 2 m;) Coupled surge and pitch motions		Effect of the frequency and amplitude of platform motions on thrust and power. Effect of the coupled surge and pitch motion on performances.	Chen et al. [86] &
NREL 5 MW WS = 7 m/s and 11.4 m/s	Surge (Amplitude 9.4 m; Period 8.1 s)	OpenFOAM/ CFD	Influence of surge motion on thrust and power. Interaction between blades and vortices.	Kyle et al. [87] &

Note: (1) “&” indicates RANS SST k-ω turbulence model; “*” indicates IDDES SST k-ω turbulence model; “--” indicates that the turbulence model used is not specified in the references. (2) Finite-volume flow solver was developed by the German Aerospace Center, with extensions for wind turbine simulations by the University of Stuttgart [78]. (3) ELSA CFD package, jointly owned by Airbus, Onera, and Safran, encompasses a RANS flow solver along with various external modules, particularly pre and postprocessing tools [81].

outlines the details of primary research on FOWTs using the forced oscillation approach. It has been identified that the aerodynamic performance of a wind turbine is most significantly affected by the surge and pitch motions of a platform [67].

After considering platform motion, research on the aerodynamic performance of wind turbines mainly focuses on two aspects: output power and thrust and the development of wake and vortices. Subsequent studies have innovatively introduced additional considerations, such as aeroelasticity, TSR, and coupled motion effects.

4.1.1. Thrust and Power

Tran et al. [68] conducted CFD simulations to analyze the behavior of wind turbine blades during pitch motion, employing the overset grid in STAR-CCM+. The study primarily focused on comparing and validating unsteady CFD codes with UBEM and FAST codes. The results indicated that, in the cases with small amplitudes of pitch motion, all numerical codes provide consistent results for thrust and power calculations. However, as the pitch amplitude increases up to 4 deg, the traditional numerical codes like UBEM and FAST exhibit certain limitations compared to the unsteady CFD codes. Subsequently, Tran et al. [70] carried out the simulations under pitch motions with different amplitudes. For pitch motion with an amplitude of 4 deg and a frequency of 0.1 Hz, complex aerodynamic phenomena, such as dynamic stall and vortex shedding, were observed in the cross-section of the blades when they moved downward. The effects of different amplitudes and frequencies under pitch motion were also demonstrated in the studies [73,75,81]. A similar analysis was conducted under surge motions with different amplitudes and frequencies [69]. The motion of a platform induced significant changes in the wake intensity, and the interaction between the blades and the wake became more evident as the frequency and amplitude increased. Fang et al. [82] conducted a study on a scaled-down (1:50) model based on an NREL 5 MW wind turbine using an IDDES model. The results indicated that the amplitudes of thrust and torque increase with the pitching amplitude and decrease with the increasing pitching period. High pitching amplitudes can lead to stall phenomena, potentially adversely affecting the aerodynamic performance of wind turbines. Moreover, the amplitude of thrust and power was significantly affected by the amplitude of surge motion, which was also supported in some studies [71,74,78].

4.1.2. Wake and Vortices

The above studies focus on motion profiles with different amplitudes and frequencies, and there are also works focusing on the characteristics of wake and vortices. Jeon et al. [88] studied the impact of pitch motion on the aerodynamic performance of wind turbines using the vortex lattice method (VLM). They observed the occurrence of a turbulent wake state (TWS) in the wind direction, which significantly affected the aerodynamic performance of a wind turbine (Figure 3). Leble et al. [76] investigated the influence of pitch motion on the aerodynamics of DTU 10 MW wind turbines. An important finding is that at a pitch angle of 5 deg, the blades enter a vortex ring state (VRS), which can be detrimental to the power output. This conclusion is further supported by the findings of Kyle et al. [87]. Under a surge amplitude of 9.4 m, vortex accumulation near the blade root was observed at 11.5 s and 12.0 s. At 12.5 s and 13.0 s, significant blade–vortex interactions occurred at the blade tip, accompanied by strong recirculation at the root, indicating the formation of VRS (Figure 4). Afterward, Kyle et al. [89] conducted a detailed analysis of the process of VRS and provided explanations for the reasons. Additionally, Feng et al. [83] discovered that a pitch angle of 4 deg can lead to the blades moving backward at a speed exceeding the wind speed, resulting in the NREL 5 MW wind turbine entering the VRS. Fu et al. [90] explored the complex interaction among the tip vortex, tower vortex, and wake under pitch motions. The amplitude and frequency had a significant impact on power and wake characteristics. When the amplitude was large, the blade tip entered the periodic wake. Sun et al. [91] investigated the coupling effect of tower shadow and surge motion on aerodynamic performances. The downwind FOWTs, with rotors being positioned on the opposite side of the wind direction, presented a heightened variability in aerodynamic loads.

4.1.3. Effect of Aeroelasticity and TSR on Aerodynamic Performance

In addition, there are studies that combine blade aeroelasticity with platform motion. Liu et al. [79] found that due to the aeroelasticity of blades, the power and thrust of the wind turbine decrease by approximately 5%. The motion of the platform causes a change in the attack angle of the wind turbine blades. An increase in the attack angle leads to an increase in lift force, which enhances the power output. Zhang et al. [92] established a CFD-FEA fluid–structure interaction model on an IEA 15 MW wind turbine, and the result indicated that the maximum tip deformation under surge and pitch motions was 1.41 times and 1.73 times that under no motions on one rotation period.

TSR is also an important factor deserving thorough research. The impact of surge motion on the thrust and power of a wind turbine was examined by Wen et al. [77], employing the FVM. The results indicated that at low TSRs, the average power decreases as the frequency decreases, while at high TSRs, it increases. At all TSRs, the average thrust decreases with increasing frequency, while power and thrust increase with the decreasing frequency and TSR. Micallef et al. [72] used ADM to model the wind turbine under surge motions with different TSR values. They compared the results with those obtained from BEM, GDW, and FAST. The results were consistent at lower TSRs, but differences emerged at higher TSRs. Corniglion et al. [80] compared the aerodynamic performance of the wind turbine under surge motions using ALM and FVM and found that both methods did not address the issue of local flow around the blades.

4.1.4. Effect of Coupled Motion on Aerodynamic Performance

The above studies primarily focused on the impact of individual motions on the dynamic performance of a wind turbine. Chen et al. [85] examined the influence of coupled motions, including yaw and pitch. They found that increasing the amplitude and frequency of the two motions deteriorates thrust and power fluctuations. The influence of pitch motion is more important than that of yaw motion, in alignment with the findings of Tran et al. [70], as mentioned earlier. Chen et al. [86] extended their research on the coupled motion of surge and pitch with varying amplitudes while maintaining the same frequencies. It was observed that increasing the frequency and amplitude of platform motion can lead to more obvious fluctuations in thrust and power. The power was identified as being more responsive to alterations in amplitude compared to alterations in frequency. Feng et al. [83] proposed a method to calculate the phase difference between pitch and surge motions, subsequently examining the influence of coupled motion with a phase difference on aerodynamic performance. The results showed that the power coefficient at 0.1 Hz is lower than that of the single motion, while the coefficient at 0.2 Hz exceeded that of the single motion, and the different combinations with varying motion amplitudes could change the locations of power variation. Guo et al. [84] conducted the simulations under conditions in which the pitch and surge motions were coupled with the same amplitude and different frequencies. The results demonstrated that the power and thrust fluctuations are more severe when compared to coupling at the same frequency.

4.2. Hydrodynamics Analysis

A partially coupled analysis of hydrodynamic performances simplifies wind loads by transferring the forces and moments to a floating platform. A summary of partially coupled analyses for FOWTs is presented in

Table 3. Summary of the partially coupled analyses on hydrodynamic behaviors of FOWTs.

Platform Model	Tool/Method	Environmental Conditions	Platform Motion	Ref.
GustoMSC Tri-Floater (NREL 5 MW)	ANSYS-AQWA PHATAS/PF and BEM	Steady Wind/Regular Wave and Irregular Wave WS = 11.4 m/s and H = 4.5 m T = 7.5 s–10 s WS = 14 m/s and H = 6.5 m T = 9–12 s WS = 25 m/s and H = 9.4 m T = 11 s–14 s	Sway Surge Heave	Huijs et al. [93]
OC4 DeepCwind	OpenFOAM/CFD	Steady Wind/Regular Wave WS = 5 m/s and H = 9.7 m T = 3.66 s WS = 7 m/s and H = 9.7 m T = 3.66 s WS = 11.4 m/s and H = 9.7 m T = 3.66 s	Pitch Heave	Zhao et al. [94]
OC4 DeepCwind	ANSYS-AQWA/PF	Steady Wind/Regular Wave and Irregular Wave WS = 11.4 m/s and H = 3 m T = 10 s	Six DOF motions	Zheng et al. [95]
VolturnUS-S	ANSYS-AQWA/PF	Turbulent wind/Irregular Wave WS = 4–47.5 m/s and H = 1.1–10.7 m T = 8.52–14.2 s	Surge Pitch Heave	Raed et al. [96]
V-shaped Semi OC4 DeepCwind Bracessless Semi	ANSYS-AQWA/PF	Steady Wind/Regular Wave and Irregular Wave WS = 8 m/s and H = 3 m T = 10 s WS = 49 m/s and H = 14.1 m T = 13.3 s	Surge Pitch Heave	Shi et al. [97]
OC4 DeepCwind	FAST/BEM	Steady Wind/Regular Wave WS = 11.4 m/s and H = 4.5 m T = 11 s	Six DOF motions	Li et al. [98]
OC4 DeepCwind	STAR-CCM+/CFD	Steady Wind/Regular Wave WS = 5 m/s and H = 9.7 m T = 3.66 s WS = 8 m/s and H = 11.3 m T = 5.49 s WS = 11 m/s and H = 13.6 m T = 9.14 s	Heave Pitch	Zhang et al. [99]
OC5 DeepCwind	OpenFAST/BEM	Elizabeth Actual Environment	Pitch	Xu et al. [100]

Note: “H” indicates the significant wave height; “T” indicates the wave peak period; “WS” indicates the wind speed.

. It can be observed that the available studies primarily focus on conditions involving steady wind with regular or irregular waves.

4.2.1. CFD

Simplifying the impact of wind turbine aerodynamic performance while modeling floating platforms is an effective strategy to reduce the computational complexity of CFD simulations. Zhao et al. [94] investigated the interplay between a floating platform and a wind turbine by an equivalent method based on OpenFOAM. The results showed that surge motion exhibits significant changes with increasing wind speed, while heave motion experiences relatively minor changes. Zhang et al. [99] used a series of moments and forces at the rotation center of the platform to simulate the impact of a rotating wind turbine on the floating platform. They used STAR-CCM+ to examine their impacts on the hydrodynamic performance under three sea conditions. The presence of the wind turbine had a small effect on the wave force and heave motion but a significant effect on pitch motion, which is consistent with the conclusions obtained from the previous study [94]. Huo et al. [101] conducted CFD simulations and physical model tests to analyze the six DOF motion responses of an X30 platform (shown in Figure 5) under the freak wave (characterized by high contingency, brief duration, and elevated peak energy level). The maximum slamming pressure induced by the freak waves was approximately 3.5 times greater than that of the normal wave slamming pressure. Moreover, the slamming pressure exerted by the freak waves on the structural system gradually decreased along the vertical direction.

4.2.2. FAST (OpenFAST)

After the release of FAST v8.16 in 2016, the development of OpenFAST was initiated to engage a broader community in enhancing the tool through additional features, updates, and ongoing improvements [102]. The time-domain software FAST (OpenFAST) can be used to assess the stability of floating platforms under different environmental conditions. In the presence of a yaw error (an angle at which turbines deviate from wind direction) and a wind–wave misalignment (an angle between the directions of wind and waves), as shown in Figure 6, Li et al. [98] conducted a reliability and safety assessment of FOWTs under various external environmental conditions at the rated wind speed based on FAST. The results indicated that the yaw error can significantly affect the power generation efficiency and safety of FOWTs. Xu et al. [100] proposed an efficient bivariate ACER2D (average conditional exceedance rate two-dimensional) method based on the Monte Carlo method. They used OpenFAST to model the mooring line tension force and blade moment of FOWTs, studying the hydrodynamic effects at the Elizabeth site, which is a station of the National Data Buoy Center in the United States.

4.2.3. ANSYS-AQWA

As a hydrodynamic analysis software based on PF theory, ANSYS-AQWA typically requires importing aerodynamic codes into its internal interface to account for the aerodynamic performance of the wind turbine. This integration enables comprehensive analysis of the coupled aero–hydrodynamic performance of FOWTs. Huijs et al. [93] used AQWA, in conjunction with the PHATAS code, based on BEM, to conduct a numerical study of the GustoMSC Tri-Floater semisubmersible. The results showed that within the wave frequency range of 0.3~2.0 rad/s, the dynamic response of the platform can be more accurately predicted through frequency domain analysis. In a low-frequency range (less than 0.3 rad/s), the partially coupled method cannot accurately simulate the motion response, requiring the use of a fully coupled method for analysis. Zheng et al. [95] conducted a dynamic response study involving all six DOF motions on OC4 DeepCwind semisubmersible. By subjecting the platform to wind forces and torques, this study was conducted under conditions where the foundation columns were filled with water. It was found that the damage to the foundation columns has a significant impact on the stability of sway and yaw motions. Shi et al. [97] conducted a performance comparison among OC4 DeepCwind, V-shaped, and unsupported semisubmersible platforms by modeling wind loads as thrust forces. The symmetry of the V-shaped platform results in a more pronounced coupling effect between pitch and heave motions. Under heave motion, the natural frequency of OC4 DeepCwind is about 0.360 rad/s, closely aligning with the wave frequency range. This may lead to significant resonances, posing a potential threat to structural safety. Raed et al. [96] used OpenFAST to generate wind loads and imported them into AQWA to study the hydrodynamic characteristics of the VolturnUS-S platform. The results were compared with those from the fully coupled method, and it was found that the partially coupled method overestimated the mooring tensions.

4.3. Summary

In partially coupled simulations, the aerodynamic behavior of wind turbines has been studied widely by employing a forced oscillation. The pitch and heave motions of a platform are predominantly studied independently, while yaw motion is examined in only a few studies. However, there is a scarcity of research on the synergistic effects of multiple motions. Typically, these aerodynamic simulations are mostly conducted at rated wind speeds, with the pitch amplitude ranging between 1 deg and 4 deg and the heave amplitude ranging from 0 m to 2 m. Next, the coupling analysis of two or three motions needs to be investigated to comprehensively analyze the effects of the complex ocean environment on aerodynamic performances.

In the hydrodynamic analysis of partially coupled simulations, there has been insufficient research on the dynamic responses of semisubmersible platforms considering the effects of aerodynamic behavior. The most available studies are performed under conditions with steady wind speed and regular waves based on CFD. Since wind farms are located within the complex, unsteady inflow of the atmospheric boundary layer, it is essential to investigate the effects of various actual wind conditions, including wind shear, wind steering, turbulent wind, gust wind, and extreme events (like storms and tropical cyclones). Applying the actual effects of wind to the platform allows for a more realistic simulation of the platform’s motion. These aspects represent potential areas for future research and exploration.

CFD simulations are used for detailed local flow modeling, accurately capturing aerodynamic and hydrodynamic effects, but at a high computational cost. ANSYS-AQWA specializes in hydrodynamic analysis of floating platforms and mooring systems, making it suitable for stability and motion predictions under wave conditions, but it has lower accuracy in simulating wind loads. FAST (OpenFAST) is an integrated and efficient tool specifically designed for offshore wind turbines. It can perform reliability and safety assessments under complex conditions, such as yaw errors and wind–wave misalignments. The choice of the appropriate tool depends on the research focus.

5. Fully Coupled Analysis

5.1. Simulation Analysis

Since partially coupled analyses simplify certain phenomena (like wind turbine rotation or platform motion), they are not sufficient to accurately simulate the interaction between wind turbines and floating platforms. In contrast, fully coupled analyses simultaneously consider environmental conditions involving both the wind and waves and model all FOWTs. The primary tools for a fully coupled analysis typically involve CFD simulations (using software such as STAR-CCM+, OpenFOAM, ANSYS, etc.), FAST (OpenFAST), and F2A. A summary of the studies conducted by the fully coupled analysis over the past decade is presented in

Table 4. Summary of the full coupled analysis of FOWTs.

Platform Model	Wind Turbine Model	Tool/Method	Environmental Conditions	Platform Motion	Ref.
OC4 DeepCwind	NREL 5 MW	OpenFOAM/CFD	Steady Wind/Regular Wave WS = 7.32 m/s and H = 3.79 s T = 12.1 s WS = 11 m/s and H = 3.79 s T = 12.1 s	Pitch Surge Heave	Liu et al. [103]
			Steady Wind/Regular Wave WS = 11.4 m/s and H = 3.79 m T = 12.1 s	Pitch Surge	Liu et al. [104]
OC4 DeepCwind			Steady Wind/Regular Wave WS = 11.4 m/s and H = 3.66 m T = 9.7 s WS = 11.4 m/s and H = 7.58 m T = 12.1 s	Pitch Surge Heave	Cheng et al. [105]
OC4 DeepCwind			Steady Wind and Turbulent wind/ Iregular Wave WS = 11.4 m/s and H = 7.58 m T = 12.1 s	Six DOF motions	Xu et al. [106]
OC4 DeepCwind			Turbulent Wind/Regular Wave Mann Turbulence WS = 11.4 m/s and H = 7.58 m T = 12.1 s	Pitch Surge	Zhou et al. [107]
					Zhou et al. [108]
			Steady Wind/Irregular Waves and Focused Waves WS = 11.4 m/s and H = 1.94–5.13 m T = 15.0 s–16.8 s	Pitch Surge	Zhou et al. [109]
OC4 DeepCwind		STAR-CCM+/CFD	Steady Wind/Regular Wave WS = 6 m/s and H = 7.58 m T = 12.1 s–14.3 s WS = 11.4 m/s and H = 7.58 m T = 12.1 s–14.3 s	Pitch	Shi et al. [110]
OC4 DeepCwind			Steady Wind/Regular Wave WS = 11.4 m/s and H = 7.58 m T = 12.1 s	Surge	Ali et al. [111]
OC5 DeepCwind			Steady Wind/Regular Wave WS = 11.4 m/s and H = 7.58 m T = 12.1 s	Pitch Surge Heave	Zhang et al. [13]
OC5 DeepCwind			Steady Wind/Regular Wave WS = 40.28 m/s and H = 16.68 m T = 13.1 s	Pitch Surge Heave Yaw	Yang et al. [112]
MARIN DeepCwind		FAST/BEM	Steady Wind/Regular Wave WS = 11.4 m/s and H = 1.92 m–11.122 m T = 7.5 s–20 s Irregular Waves WS = 11.4 m/s and H = 10.5 m T = 14.3 s	Pitch Surge Heave	Hall et al. [113]
OC4 DeepCwind			Steady Wind/Regular Wave WS = 11 m/s and H = 7.58 m T = 12.1 s	Six DOF motions	Tran et al. [114]
OC4 DeepCwind			Steady Wind/Irregular Waves WS = 15 m/s and H = 7.1 m T = 12.1 s	Six DOF motions	Bae et al. [115]
OC4 DeepCwind			Steady Wind/Regular Wave and Irregular Wave WS = 8 m/s and PSD = 1 m2/Hz for 0.05–0.25 Hz	Six DOF motions	Liu et al. [116]
OC4 DeepCwind		FAST/BEM	Steady Wind/Regular Wave and Irregular Wave WS = 60 m/s and H = 9.1 m T = 12.7 s	Heave	Yang et al. [117]
VolturnUS-S	IEA 15 MW	FAST/BEM	Turbulent wind/Irregular Wave WS = 12 m/s and H = 1.84 m T = 7.44 s WS = 47.5 m/s and H = 10.7 m T = 14.2 s	Surge Sway Pitch Yaw	Niranjan et al. [118]
OC3 Hywind	NREL 5 MW	FAST and AQWA/ BEM and PF	Steady Wind and Turbulent wind/ Regular Wave WS = 4 m/s–25 m/s and H = 1.6 m–4.0 m T = 3.4 s–8.8 s	Pitch Surge Heave	Yang et al. [119]
V-shaped Semi Triangle-shaped Semi		FAST and AQWA/ BEM and PF	Steady Wind/Irregular Waves WS = 55 m/s and H = 10.1 m T = 17 s	Six DOF motions	Shen et al. [120]
OO-Star	DTU 10 MW	FAST and AQWA/ BEM and PF	Turbulent wind/Irregular Wave WS = 11.4 m/s and H = 6.97 m T = 10.4 s	Pitch Surge Heave	Chen et al. [121]

Note: PSD indicates the power spectral density.

. It can be found that these studies have primarily been conducted under the conditions of steady wind and regular waves, focusing on three main motions of a platform (surge, pitch, and heave). The research advancements in fully coupled numerical simulations for FOWTs are discussed in this section.

5.1.1. CFD

Liu et al. [103] conducted a comprehensive examination of the NREL 5 MW wind turbine using OpenFOAM, exploring the flow patterns surrounding its blades. As depicted in Figure 7, the presence of strong vortices in close proximity to both the blade tips and roots is clearly illustrated. Subsequently, Liu et al. [104] developed a sophisticated, fully coupled analysis tool encompassing aero–hydro–mooring–elastic aspects, incorporating the elasticity of blades. They studied the interactions between different components. The results indicated that, in moderate wave heights, there is a significant variation in torque, but the effect of elastic blades on the RAOs of the platform is relatively small. Zhang et al. [13] performed a fully coupled simulation of an NREL 5 MW wind turbine supported on OC5 DeepCwind semisubmersible. They conducted a detailed analysis of the thrust, power, blade tip vortices, and wake. The RAOs of heave, pitch, and surge motions were higher than those of OC4 DeepCwind semisubmersible. Cheng et al. [105] conducted a fully coupled simulation of NREL 5 MW wind turbine mounted on OC4 DeepCwind semisubmersible based on the coupled solver FOWT-UALM-SJTU, which was developed in OpenFOAM and integrated with the ALM model. Compared with other traditional CFD models, this numerical model significantly reduces computational time. Xu et al. [106] also conducted a numerical simulation study of a FOWT under complex atmospheric inflow conditions using the ALM model. The results showed that the oscillation amplitude of the wind turbine output increased compared to uniform inflow under complex atmospheric inflow conditions, and there was a significant difference in the yaw motion. Shi et al. [110] studied the impact of pitch motion, employing the DFBI (Dynamic Fluid–Body Interaction) method to simulate the interactions between the platform and the wind turbine. They found that the wind speed has a greater impact than the wave period under pitch motion. Ali et al. [111] investigated the aerodynamic effects of surge motion under fully coupled wind and wave load conditions. The results indicated that turbines with surge motion could be placed closer together compared to fixed turbines, which is critical for minimizing wake interference and optimizing the overall efficiency of wind farms. Yang et al. [112] studied the differences in results between the CFD method and the medium-fidelity tool based on potential flow theory under extreme environmental conditions. Significant differences were observed between the two tools in terms of the surge motion.

Zhou et al. [107] conducted a coupled analysis of an aero–hydro–mooring FOWT system using the Mann turbulence model in OpenFOAM under conditions with turbulent wind and wind shears. The results revealed that the two factors have an insignificant impact on the mooring tension, dynamic motion, and inline surge force. This conclusion is further validated by the subsequent research [108]. Furthermore, Zhou et al. [109] investigated the effects of wave types and wave steepness on the dynamic behavior of FOWTs, using focused waves (created by linearly superimposing a series of regular waves) and irregular waves. It was found that the wave type and wave steepness have a minimal impact on the aerodynamic performance of FOWTs. Huang et al. [122] designed a biomimetic semisubmersible platform, as illustrated in Figure 8, and conducted a fully coupled analysis. It was indicated that the new platform can increase the power output of the wind turbine while mitigating the platform’s motion response. The authors attributed this improvement to the energy dissipation caused by vortices within the fractal structure. These vortices absorbed the mechanical energy excited by wind–wave excitation through friction and collisions with the walls, thereby enhancing the stability of the platform.

5.1.2. FAST (OpenFAST)

FAST is also a tool widely used to conduct a fully coupled analysis. Tran et al. [114] conducted a comprehensive analysis that couples aero–mooring–hydro elements for the NREL 5 MW wind turbine supported on OC4 DeepCwind semisubmersible, combining the DFBI method with FAST. They also employed GDW to visualize the complex wake generation around the blade roots, hub, and tower. It can be clearly observed that both the wind and waves simultaneously induce the motion of a FOWT, showing the presence of strong interactions between different components. Bae et al. [115] performed a dynamic response analysis on an OC4 DeepCwind semisubmersible platform with a broken mooring line. The results indicated that the drift distance due to the broken mooring line can exceed 700 m, posing a significant risk to other FOWTs nearby. Yang et al. [117] simulated the motion response after all three mooring lines were broken, and it was nearly impossible to rescue the platform during the drift process. Niranjan et al. [118] conducted a study on the accidental failure of redundant (six-line) and non-redundant (three-line) mooring systems for the VolturnUS-S platform in extreme environments. The results indicated that the six-line mooring configuration of FOWT performed better after the failure of one mooring line, with the platform experiencing smaller drifts. Additionally, Hall et al. [113] conducted the numerical simulations using a lumped-mass mooring model in FAST and subsequently validated the results by the data in the MARIN DeepCwind test. When taking irregular waves into account, the extreme and fatigue loads were within 10% of the experimental results. Liu et al. [116] suggested the implementation of a single-point mooring (SPM) system on OC4 DeepCwind semisubmersible FOWT and conducted a comparative analysis with a multi-point mooring (MPM). The wind had a significant effect on the motion of sway, yaw, and roll of the SPM. Berdugo et al. [123] proposed a method for extending the single wind turbine solver based on OpenFAST and conducted a coupled performance analysis of multi-wind turbines.

5.1.3. F2A

Recently, a coupled framework, F2A, was developed by Yang et al. [119] based on FAST and AQWA to conduct a fully coupled analysis of FOWT. This method integrates the advantages of FAST in simulating aero–servo–elastic and AQWA in simulating nonlinear hydrodynamics and mooring dynamics. The results were compared with OpenFAST to validate the accuracy of this method. The capabilities of FAST were successfully implemented in AQWA. Shen et al. [120] employed this method to conduct a fully coupled time-domain simulation on two different semisubmersible platforms, and the results indicated that the stability of the triangular platform is superior. Chen et al. [121] used F2A to establish a fully coupled time-domain model for the OO-Star platform and investigated the hydrodynamic performance of the platform at different water depths in China’s shallow to medium waters. The results showed that although the trend of the RAO remained consistent, the peak values increased as the water depth decreased. Many other studies have also validated the F2A method, including analyses of the barge-type floating platform [124], designs of a new floating support platform [125,126], and the application of hybrid systems combining wind and wave energy [127,128]. Some of the studies (F2A) mentioned did not utilize semisubmersible platforms. Therefore, their methods and key parameters are not included in Table 4.

5.2. Summary

In fully coupled simulations, OpenFAST (formerly known as FAST), F2A and CFD serve as the primary tools for precisely simulating FOWT systems. OpenFAST is an open-source software with simplified physical models, resulting in certain limitations in simulating flow fields and structural response simulations that require high fidelity. Moreover, it was initially developed for the NREL 5 MW turbine, and therefore, when applied to other wind turbine models, it may encounter issues that require further development and adjustments. Specifically, when OpenFAST is used for different wind turbine models, parameterization and calibration are typically needed to ensure the accuracy and applicability of the model. Additionally, supplementary control systems may be required to improve its functionality and adapt it to different operating conditions. The F2A method can exhibit some unique phenomena as opposed to conventional tools, and the new coupling framework can be utilized for the analysis of FOWT. CFD methods are widely employed due to their capacity to capture detailed flow fields with high accuracy. Due to the complexity of fully coupled models, the actuator line model (ALM) is utilized to simplify the modeling of wind turbines and reduce time-consuming. However, it is important to note that, despite significant progress in the development of fully coupled models, there remains limited analysis of the coupling between aerodynamics, hydrodynamics, and structural dynamics. Future investigations should focus on optimizing techniques for fully coupled simulations, as this constitutes a critical area of further study.

6. Physical Model Tests

The physical model test involves using scaled models in wind tunnels and wave basins to simulate the dynamic behavior of FOWTs, allowing for the study of their performances, stability, and the impact of environmental loads. When conducting scaled physical model tests, selecting appropriate scaling laws and designing the test models and environments accordingly are crucial for accurately studying their dynamic performances. Wind tunnel model tests of fixed wind turbines and wave basin model tests of floating offshore structures provide important references for physical model tests of FOWTs. However, due to the complexity of FOWT structures and the uniqueness of their environments, relevant theories and research techniques cannot be directly applied to physical model tests of FOWTs. A significant challenge in physical model tests lies in addressing the scale effect, where achieving a proper match of the Froude number and Reynolds number between the two fluid domains (air and water) can be complex.

To accurately experiment with the aerodynamic and hydrodynamic performance of FOWTs, the selection of similarity criteria is particularly important. Generally, it is necessary to satisfy geometric similarity, Froude number similarity, and Reynolds number similarity.

(1) Geometric similarity: In physical model tests, all linear scale parameters must meet the condition of geometric similarity, such as blade diameter, tower height, platform water depth, center of gravity, and wave height. The geometric similarity can be expressed as follows:

$${\delta }_l={\displaystyle \frac{l_n}{l_{\mathrm{m}}}}$$

(6)

where $\delta$ is the length scale factor, l_n is the prototype length, and l_m is the model length.

(2) Froude number similarity: In wave basin tests, the emphasis is on simulating gravity and inertia forces. It is typically necessary to ensure that there is a correct proportional relationship between inertia and gravity forces, with priority given to considering the Froude number. The Froude number is defined as follows:

$$Fr={\displaystyle \frac{u}{\sqrt{\mathrm{g}l}}}$$

(7)

where u is the velocity of the fluid, l is the characteristic length of the body in the fluid field, and g is the gravitational acceleration.

(3) Reynolds number similarity: In wind tunnel tests, the emphasis is on simulating viscous and inertial forces. Ensuring the correct proportional relationship between inertial and viscous forces is key to accurately replicating blade flow patterns and aerodynamic performance. Therefore, priority should be given to considering the Reynolds number. The Reynolds number is defined as follows:

$$Re={\displaystyle \frac{\rho ul}{\mu }}$$

(8)

where ρ is the density of the fluid, and µ is the dynamic coefficient of viscosity.

This paper describes the physical model tests of FOWTs based on three aspects, depending on the test object and environment: wind tunnel tests, wave basin tests, and real-time hybrid tests.

6.1. Wave Basin Tests

Froude number similarity is usually preferred in wave tests, as the primary factor influencing hydrodynamic loads is gravity rather than viscous forces. The test conditions mainly consist of two types: static water conditions and wave conditions. The former includes tests such as single DOF motion decay tests and mooring system horizontal stiffness tests. The latter mainly includes regular wave tests and irregular wave tests. The combined wind and wave test is the core part of the wave basin test, which aims to directly obtain the hydrodynamic performance under real sea conditions.

6.1.1. DeepCwind Project

The most typical tests are from the DeepCwind project. As the leading projects, phase IV of OC3, phase II of OC4, phase II of OC5, and phase I of OC6 represent the most representative work of the NREL. The main objective of this project is to provide comprehensive and accurate data to support the validation and further development of FOWT-coupled simulators. In OC4, tests were conducted on three types of 1:50 scaled-down FOWTs, including a spar, a semisubmersible, and a tension leg platform, scaled primarily using Froude number and geometric similarity. Goupee et al. [129] studied the motion responses under various environmental conditions, encompassing regular and irregular waves, static and dynamic wind, and combinations of extreme conditions, where the scaling of wind and waves employs Froude number similarity. The tests did not achieve the expected results because the scaled-down wind turbine did not obtain the corresponding thrust at a low Reynolds number. They adopted an approach of increasing the wind speed to match the required Reynolds number. Subsequently, to address the issues with wind turbine performance observed in the previous experiment, a new wind turbine (MARIN Stock Wind turbine) was constructed, and the tests were re-conducted. The results obtained the global response of FOWTs [130]. Hall et al. [113] modeled the mooring line using a lumped-mass mooring line model and validated the numerical results against the test results using FAST. The mooring line model was improved in the OC5 project, and Helder et al. [131] performed a wave basin test on a 1:50 scale-down FOWT. Then, Robertson et al. [12,132] validated the numerical model in FAST based on the test results. Results from OC5 indicated an underestimation of low-frequency pitch and surge motions of FOWTs (averaging around 20%), although it was uncertain whether this discrepancy fell within the range of experimental errors. To gain a better understanding of nonlinear hydrodynamic loads, two new wave basin tests were conducted. In the first test, the wind turbine was removed, and thrust was ignored. Measures such as using a rigid tower and simplified mooring lines were implemented to reduce uncertainties in the response behavior. Repeated tests were conducted to examine the repeatability of the low-frequency response of the FOWT [133]. In the second test, a platform model without a tower was employed to separately study the effects of diffraction and radiation hydrodynamic loads [134]. Both tests were conducted in the wave basin at MARIN. To assess the limitations of existing engineering models, the OC6 project employed CFD [60] to provide three-way comparative verification, offering further insights into the nonlinear hydrodynamic loads on FOWTs. However, due to discrepancies in simulation time, a new test was conducted in 2021 [135]. They further simplified the semisubmersible platform by removing small cross-members and the main column while retaining the offset columns with heave plates. This was because larger components had the greatest impact on the overall load of the FOWT. Individual components were tested separately, followed by the addition of other components to enhance the completeness of the test. The results provided validation for the nonlinear hydrodynamic loading on the semisubmersible platforms [136,137,138].

6.1.2. Other Aspects

Other wave basin tests also used the Froude-scaled. In 2010, Roddier et al. [139] conducted a combined wind and wave test on Wind Float at a scale of 1:105, selecting conditions for a once-in-a-century storm and comparing the results with simulations. Overall, the test did not demonstrate the limitations of numerical simulation tools. In 2013, Ridder et al. [140,141] conducted physical model tests on the Tri-Floater at a scale of 1:50, drawing from the experience of phase IV of OC3. They redesigned the model blades to meet “performance similarity” criteria for low Reynolds number blades. This physical model test marked the first to use a remote blade pitch control system, but significant discrepancies were found between the test and simulated results. Guo et al. [142] conducted a study on a semisubmersible platform supporting a 12 MW wind turbine under various sea states, including regular waves, white noise, irregular waves, wind–wave, wave–current, and wind–wave–current conditions, to evaluate the motion response and mooring system loads. Goupee et al. [143] developed a new platform called VolturnUS-S, using a 1:8 scale model made of concrete, which was tested on an offshore test site. Their main objective was to collect aero–elastic–hydrodynamic coupled numerical model validation data on a nearly full scale. Fowler et al. [144] also conducted tests on the model platform (1:70) with the addition of a control system. The results indicated that the pitch motion was significantly reduced, leading to an increase in wind turbine power output. Subsequent research also focused on the load characteristics of the scaled model under focused wave conditions [145].

6.2. Wind Tunnel Tests

In order to study the aerodynamic performance of FOWTs and the evolution of wake and vortex, as well as to provide reliable data support for the validation of various numerical models, many scholars have conducted extensive field measurements and wind tunnel tests.

6.2.1. UNAFLOW Project

A more comprehensive test is the UNAFLOW project within the EU LIFES50+ initiative. The objective of this undertaking was to study the aerodynamic characteristics of FOWTs in order to evaluate the aerodynamic code used in the project [146], with a focus on the DTU 10 MW wind turbine. The scaled parameters for the wind turbine model include characteristic length, TSR, and thrust coefficient. In 2013, Bayati et al. [147] conducted a 2-DOF wind tunnel test using a 1:25 scale model to simulate the effects of platform motion on the wind turbine. This was expanded to 6-DOF in 2014 [148], with results compared to the previous findings. Subsequently, Bayati et al. [149] used a 1:75 scale model to investigate wind tunnel tests under surge and pitch motions, validating them against numerical results. The following year, Bayati et al. [150] explored the impact of surge motion with different frequencies and amplitudes on the wake. In subsequent projects, such as phase IV of OC6 [151], numerical simulations were conducted under large platform motions and validated against earlier test results, showing a good agreement.

Similar experiments include those conducted by Rockel et al. [152] using stereo particle image velocimetry to measure the wake evolution of a 1:400 scale wind turbine and investigate the influence of pitch motion. Additionally, Hu et al. [153] utilized the same method to study the performance and near-wake characteristics of a 1:300 scale model turbine. Messmer et al. [154] conducted a study on the wake of a wind turbine experiencing pitch motion under low turbulence intensity. The results showed that as the turbulence intensity increased, the influence of motion on the recovery of the wake gradually decreased.

6.2.2. Optimization of Scaled Wind Turbine Models

The tests mentioned above used scaled-down prototype wind turbines, while some tests employed wind turbines with similar performance characteristics. Through certain adjustments, the model blades were redesigned to have a similar aerodynamic performance as the prototype blades. The core of this approach is to substitute low Reynolds number airfoils for the high Reynolds number airfoils in the prototype while also redesigning the chord length and twist distribution of the blades. The similarity criteria are mainly geometric similarity and Froude number similarity. Better simulation results on the aerodynamic performance of FOWTs can be obtained by this method, which is regarded as one of the solutions to reduce the scale effect.

Cao et al. [155] designed the performance-matched blades to replace the scaled-down DTU 10 MW wind turbine to validate the new floating platform they proposed. Wang et al. [156] used the NACA 4412 airfoil to replace the NREL 5 MW wind turbine, which can more accurately reflect the aerodynamic performance of the model wind turbine compared to traditional methods. Luo et al. [157] also used the same airfoil and verified it against the FAST, which shows that this design can be applied to the physical model tests of the NREL 5 MW wind turbine.

6.3. Real-Time Hybrid Tests (RTHT)

RTHT offers a comprehensive solution to scale effect by combining the authenticity of model tests with the effectiveness of numerical simulations. RTHT delves into the dynamic characteristics of intricate engineering systems by merging virtual and physical components. In RTHT, an actual subsystem dynamically interacts in real time with a computer model that embodies a virtual subsystem. The incorporation of interface boundary conditions between these two subsystems is notably facilitated through the use of an actuator system.

6.3.1. RTHT Based on Numerical Wind Tunnel

This RTHT method employs the aerodynamics of the wind turbine as a virtual subsystem, while the hydrodynamic performance of the platform serves as an actual subsystem. An actuator system is used to replace the wind turbine model, forming a numerical wind tunnel to simulate the aerodynamic performance of FOWTs. This approach reduces the complexity of the physical model while increasing the factors affecting aerodynamic loads.

Hall et al. [158] employed FAST to simulate the aerodynamic performance of the NREL 5 MW wind turbine, and they used an actuator system to simulate the aerodynamic loads as scaled forces acting on the platform. The motion of the platform was inputted into the wind turbine simulation, and the forces generated by the simulation were fed back to the platform. The results were analyzed against OC3 test data, leading to the establishment of performance specifications for RTHT based on numerical wind tunnel tests.

The actuator system can be realized in several ways. In earlier work by Azcona et al. [159], a single fan was utilized to replace the wind turbine, introducing a variable force to represent the total thrust of FOWT, while the numerical wind tunnel provided the required thrust. This method was termed as software-in-the-loop (SIL). A test was conducted on a 1:40 scaled-down version of the OO-Star platform, focusing on the pitch and surge motions. The results were compared with numerical simulation results, thus confirming the feasibility of the SIL method. Similarly, Matoug et al. [160] compared the differences between the tests conducted with and without the SIL method for DTU 10 MW wind turbine. The results showed that the data in the tests using the SIL method were more consistent with numerical simulation results. Pires et al. [161] used a multi-propeller drone instead of an actuator to improve the performance of the scaled model in a wave basin test. Sauder et al. [162,163,164] proposed an actuator system based on multi cables, which consists of six cables with pulleys arranged within a square frame. This system can simultaneously simulate axial thrust, horizontal lateral aerodynamic forces, rotor torque, pitching, and yawing moments. The results demonstrated the excellent performance of this system. Based on the test data, they standardized numerical models for unsupported FOWTs. Karimirad et al. [165] compared the results of the RTHT with those of numerical simulations and found a good agreement between them. Hall et al. [166] used an actuation system with a cable to test a 1:50 scaled-down version of the OC4 platform, demonstrating the applicability of this method.

In recent years, there has been increasing interest in actuation systems based on combined fans. Guanche et al. [167] proposed a new actuation system comprising six combined fans, demonstrating that besides thrust and torque, other important aerodynamic loads can also be simulated. Otter et al. [168] utilized six drones and a custom-designed frame to represent the actuation system. They achieved a good result for the aerodynamic loads of the NREL 5 MW wind turbine. Similarly, Ha et al. [169] employed six asymmetric fans to simulate the aerodynamic loads of a DTU 10 MW wind turbine.

The above studies have sufficiently demonstrated the feasibility and superiority of the RTHT method based on numerical wind tunnels. However, there are still shortcomings in terms of the accuracy and real-time reproduction of aerodynamic loads of wind turbines, as well as the dynamic evolution of wake.

6.3.2. RTHT Based on Numerical Wave Basin

This RTHT method uses the hydrodynamic performance of the platform as a virtual subsystem and the aerodynamics of the wind turbine as an actual subsystem. SIL is used to replace the platform, forming a numerical wave basin to simulate the hydrodynamic performance of the platform. This constitutes a coupled system between the numerical platform and the physical wind turbine.

There are also some studies on this topic in the EU LIFES50+ project. Bayati et al. [170] investigated the hydrodynamic performance of the OC5 platform, simulating pitch and surge motions and transmitting the responses of the platform to the physical wind turbine. Then, they removed gravity and inertia forces from the aerodynamic loads of the wind turbine, achieving more accurate feedback. Ambrosini et al. [171] elaborated on the technical aspects and themes of the research. Bayati et al. [172,173] used the same approach to extend the 6-DOF systems.

Thys et al. [174] first performed wind tunnel tests on a DTU 10 MW wind turbine and tested it with a 6DOF robotic connection. Then, the validated aerodynamic model was used to simulate aerodynamic loads as an actuator system to correct the wave basin test of the platform. Fontanella et al. [175] also utilized a 6-DOF robot to represent the motions of the platform and investigated the aerodynamic performance and global response of the IEA 15 MW wind turbine.

6.4. Summary

Conducting model tests in wave basins or wind tunnels is a cost-effective and highly efficient method, providing valuable references for numerical simulation results. Traditional wave basin tests are limited by the challenges of accurately matching aerodynamic loads on the wind turbine, resulting in lower adaptability and reduced fidelity due to scale effects when replicating coupled dynamic fields. On the other hand, wind tunnel tests often employ forced oscillation methods to simulate the impact of platform motion on turbine performance. However, this approach focuses only on specific key motions while neglecting other relevant motions. RTHT provides a solution to these issues, with the key challenge lying in the real-time interaction and coupling between physical and numerical spaces. There are high demands on the computational speed of numerical models and the execution speed of the actuators system, but the existing research has not fully eliminated the system time delay caused by hardware and software execution. Furthermore, there are still shortcomings in accuracy and real-time performance within the numerical domain, as well as in controlling dynamic response performance. Therefore, comprehensive performance testing and evaluation of the system are required.

7. Conclusions and Challenges

7.1. Conclusions

As offshore wind energy planning and installations progress into deeper and more remote waters, floating offshore wind turbines (FOWTs) have become a key research focus due to their economic viability in deep-water regions. FOWTs represent a complex dynamic system, and this review primarily discusses the intricate interactions between aerodynamics and hydrodynamics. To accurately model and predict the aero–hydrodynamics behavior of FOWT systems, various analytical methods have been developed. This paper focuses mainly on research articles utilizing CFD (FLUENT, STAR-CCM+, OpenFOAM), FAST (OpenFAST), and ANSYS-AQWA, while other simulation software, such as HAWC2, WAMIT, and internal open-source codes, are excluded. Based on the level of coupling involved, the analysis can be categorized into uncoupled, partially coupled, and fully coupled analyses. This study mainly reviews the latest advancements in existing numerical research over the past decade and classifies them according to these three types.

In uncoupled analysis, aerodynamic and hydrodynamic are considered independently. This approach is simple and efficient in calculations, making it suitable for the preliminary design stage. However, it fails to accurately capture the interactions between subsystems. Partially coupled analysis takes into account some coupling effects between aerodynamics and hydrodynamics, allowing it to capture partial feedback and providing higher accuracy compared to uncoupled analysis. It is suitable for design optimization stages with moderate computational costs. Nevertheless, this method overlooks certain critical interactions, limiting its effectiveness in predicting extreme sea conditions. Fully coupled analysis provides the most accurate representation of the dynamic response of FOWTs. This method is particularly suitable for performance evaluation under complex sea conditions. However, it involves complex modeling, high computational costs, and requires high-performance computing resources. Therefore, selecting an appropriate coupling analysis method based on the requirements at different design stages is crucial to balancing accuracy and computational efficiency.

In addition to numerical methods, aero–hydrodynamics physical model tests are crucial for understanding the behavior of FOWTs. Each type of model test serves different purposes in evaluating FOWT behavior. Wave basin tests are ideal for hydrodynamic and mooring system analysis but struggle with aerodynamic coupling and scale effects. Wind tunnel tests are good for detailed aerodynamic analysis but are limited in representing complete platform motions and coupled effects. RTHT offers an integrated and flexible approach but faces challenges in synchronization, technical complexity, and real-time control.

Based on the above conclusions, the gaps between existing research and potential future directions can be categorized into two major aspects. The first is the lack of comprehensive and high-quality experimental data. Due to cost constraints and idealized experimental setups, many experiments fail to fully capture the complexities of real sea conditions. Future research should focus on integrating large-scale experimental data and field tests to validate simulation results and enhance their applicability. The second aspect is the limitations of fully coupled model studies. While significant progress has been made in uncoupled and partially coupled methods, fully coupled simulations remain insufficiently explored. The development of fully coupled models that are both comprehensive and computationally efficient is crucial for accurately predicting the behavior of FOWTs under real sea conditions. This paper focuses on the coupled aero–hydrodynamic simulation analysis of FOWTs, emphasizing the importance of developing fully coupled models. The research indicates how to further develop the models, which is the focus of the next section.

7.2. Future Trends and Challenges

Develop and improve numerical models that can fully couple multiple disciplines, including aerodynamics, hydrodynamics, structural dynamics, and control systems, to enhance the prediction accuracy of FOWTs’ dynamic responses under complex environmental conditions. This involves creating more refined, fully coupled models and efficient algorithms to address multi-scale challenges. The future development directions for the coupled aero–hydrodynamics research of FOWTs can be summarized as follows:

Coupling effect analysis under extreme sea conditions: Future research should strengthen the analysis of the coupled aero–hydrodynamics effects under extreme sea conditions (such as typhoons and freak waves), with particular emphasis on considering nonlinear and transient phenomena in the coupled models. This is crucial for enhancing the safety and stability of wind turbines under extreme conditions.

Blade aeroelastic analysis: As the current wind turbine capacity reaches 22 MW and continues to increase, the aerodynamic elastic issues associated with longer and slender blades are becoming more prominent. At higher wind speeds, significant bending and twisting can occur, leading to potential blade failure and fatigue damage. Therefore, developing high-fidelity aeroelastic numerical models is crucial to accurately predict and mitigate these issues.

Design of floating platforms for specific marine regions: Designing floating platforms for specific marine regions requires addressing unique environmental challenges and operational requirements. For example, in the South China Sea, where typhoons occur frequently, innovative platform designs are crucial to enhancing stability. This involves considering various platform configurations and combining the characteristics of different platform types to leverage their advantages. Additionally, optimizing mooring systems and incorporating advanced damping mechanisms can significantly improve platform performance under extreme conditions.

Hybrid wind and wave energy systems: Hybrid wind and wave energy systems combine FOWTs and wave energy converters (WECs), harnessing both wind and wave energy. These systems aim to enhance the efficiency and reliability of renewable energy generation in marine environments by leveraging the complementary characteristics of wind and wave energy. Integrating wave energy converters into the system can improve the stability of offshore platforms by reducing the impact of wave forces, thereby optimizing the structural design of the combined system. Additionally, the vertical oscillatory motion of waves can provide extra power for WECs.

Wake effect between multiple FOWTs: When multiple FOWTs operate together in an offshore wind farm, the wake effect can significantly impact the overall performance and efficiency of the wind farm. Variations in wind speed and direction can influence the intensity and extent of the wake. Higher wind speeds can quickly recover the wake velocity, while adverse changes in wind direction may exacerbate the wake effect. A well-planned layout can also mitigate the impact of wakes, thereby improving overall energy generation efficiency.

Research in the directions outlined above can effectively drive advancements in the coupled aero–hydrodynamic performance of FOWTs. This review examines recent advancements in the coupled aero–hydrodynamic numerical simulations for horizontal-axis FOWTs with semisubmersible platforms, highlighting the use of CFD, FAST (OpenFAST), and ANSYS-AQWA, and relevant physical model tests. Applications of other popular software, such as HAWC2, WAMIT, and internal open-source codes, are not covered in this paper, nor are the studies on the other FOWT types. In addition, FOWTs represent a coupled areo–hydro–servo–elastic–structural–mooring complex system. The structural dynamics and servodynamics are beyond this study’s scope. Future work will emphasize the integrative nature of the FOWT system, as there is still a significant amount of work to be conducted in this area.