OMAE A COMPARISON OF TWO COUPLED MODEL OF DYNAMICS FOR OFFSHORE FLOATING VERTICAL AXIS WIND TURBINES (VAWT)

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1 Proceedings of the ASME rd International Conference on Ocean, Offshore and Arctic Engineeing OMAE2014 June 8-13, 2014, San Francisco, California, USA OMAE A COMPARISON OF TWO COUPLED MODEL OF DYNAMICS FOR OFFSHORE FLOATING VERTICAL AXIS WIND TURBINES (VAWT) Michael Borg Cranfield University Cranfield, MK43 0AL, UK Kai Wang NOWITECH/CeSOS, NTNU Trondheim, Norway Maurizio Collu Cranfield University Cranfield, MK43 0AL, UK Torgeir Moan NOWITECH/CeSOS, NTNU Trondheim, Norway ABSTRACT As part of the deployment of floating offshore wind turbines (FOWTs) in deep sea, robust coupled dynamic design codes based on engineering models are being developed to investigate the behaviour of FOWTs in the offshore environment. The recent re-emerging interest in vertical axis wind turbines (VAWTs) for floating foundation applications has resulted in a number of design codes being developed concurrently by different researchers. In this study, two such design codes for floating VAWTs developed at Cranfield University and the Norwegian University of Science and Technology are compared through a series of increasingly complex simulation load cases. A floating VAWT design was specified to be used in this study. The rotor is based on the Darrieus Troposkein shape and is the same used within the DeepWind VAWT spar concept, with a 5MW rated capacity. The floating support structure is a semi-submersible that is being used in the Offshore Code Collaboration Continuation (OC4) Phase II project for floating horizontal axis wind turbines. A series of load cases were set out to assess and compare the two different design codes. A comparison of the performance of the two design tools is presented, illustrating their level of maturity and areas of improvement. KEYWORDS: FOWT; VAWT; Coupled dynamic design codes; Comparison. INTRODUCTION The need to further exploit offshore wind resources to increase renewable energy generation has pushed offshore wind turbines into waters deeper than 50m, where floating support foundations become more economically viable than fixed support foundations [1]. To develop a thorough understanding of the behaviour and operation of floating offshore wind turbines (FOWTs) in the harsh offshore environment, robust coupled dynamics design codes built upon engineering models are currently being developed by a number of institutions. Verification and validation of these design codes through codeto-code comparison and experimental tests are essential to gain confidence and further aid the development of such codes. The recent re-emerging interest in vertical axis wind turbines (VAWTs) for floating foundation applications (see e.g. Shires [2]) has resulted in a number of design codes being developed for this class of turbine concurrently by different researchers. In this study, two coupled dynamics design codes for floating VAWTs (FVAWTs) developed at Cranfield University and the Norwegian University of Science and Technology (NTNU) are compared through a series of increasingly complex simulation load cases. The objectives of this study are to assess similarities and differences between the two design codes to establish areas of improvement and current limitations, and initiate an international collaboration to bring forward the FVAWT concept and accelerate its deployment. A FVAWT design proposed by Wang [11] was used throughout all simulations. The rotor is based on the Darrieus Troposkein shape and is the same used within the DeepWind VAWT spar baseline concept, with a 5MW rated capacity [3]. The floating support structure is a semi-submersible that is being used in the Offshore Code Collaboration Continuation (OC4) Phase II project for floating horizontal axis wind turbines [4]. DESIGN CODES Brief descriptions of the coupled models of dynamics involved in this study are given below. 1 Copyright 2014 by ASME

2 FloVAWT by Cranfield University FloVAWT (Floating Vertical Axis Wind Turbines) is a coupled dynamics design tool currently being developed to investigate the technical feasibility of FVAWTs. It is being developed as part of the EU-funded FP7-H2OCEAN project to design a multiple-use offshore platform incorporating wind and wave energy harvesting. The aim of FloVAWT is to have a simplified coupled dynamics design tool for use in the preliminary design stages of FVAWTs. The MATLAB/Simulink programming environment is currently being used as the basis of FloVAWT. Continual development and verification of FloVAWT has been reported by Collu et al. [5, 6]. Aerodynamics: the Double Multiple Streamtube (DMS) momentum model has been implemented with several modifications to include platform motion, the Gormont-Berg dynamic stall model, Reynolds number variation, tower shadow, tip losses and turbulent incident wind (generated by TurbSim [7]). More details can be found in Collu et al. [5, 6]. Hydrodynamics: The Cummins equation is implemented with a radiation-force state-space approximation to enhance computational efficiency in the Simulink environment, based on the Marine Systems Simulator toolbox [8]. SESAM/Wadam is used to generate frequency-dependent first-order wave excitation and mean drift forces and hydrodynamic coefficients, which are then utilised in the Simulink model. A linear viscous damping model is also implemented. Validation of this approach has been reported by Collu et al. [5, 6]. Mooring line dynamics: In this study a linearised forcedisplacement relation is implemented, obtained from linearization of the quasi-static catenary model about the equilibrium position. Structural dynamics: FloVAWT currently does not incorporate a structural model and makes use of the rigid body assumption, although an analytical formulation representing gyroscopic forces and the variation of Ixx and Iyy moments of inertia of the system due to the rotation of the rotor are included. Generator/Transmission/Control modelling: In this study a constant rotational speed was considered, and as a generator/transmission/control model has not yet been implemented in FloVAWT. Simo-Riflex-DMS by NTNU Simo-Riflex-DMS is a recently developed non-linear simulation tool for modeling FVAWTs. It incorporates integrated models of the wind inflow, aerodynamics, hydrodynamics, structural dynamics and controller dynamics so as to form an aero-hydro-servo-elastic simulator in the time domain. The Simo-Riflex wind turbine module has been previously validated [9; 10], and the Simo-Rilfex-DMS was presented and verified [11]. Aerodynamics: The aerodynamic module is based on the Double Multiple Streamtube (DMS) model with including the effects of Reynolds number variations and incorporating the Beddoes-Leishman dynamic stall model. Turbulent wind field is generated by TurbSim. The code has been validated by comparison with existing experimental data [12]. The relative velocity sums up incoming wind velocity at certain point and blade element velocity induced from platform motion and blade elastic deformation. Hydrodynamics: The floater was considered as a rigid body and Simo [13] calculated the rigid body hydrodynamic forces and moments on the floater. The hydrodynamic loads on the floater are handled by linear potential flow model plus the viscous term of the Morison formula, while the hydrodynamic loads on the mooring lines are considered by Morison formula. The potential flow model produced added mass, radiation damping and first order wave forces in Wadam. The frequencydependence added mass and damping for radiation force are included in the Commins equation which will be solved by introducing the retardation function. The retardation function involves a convolution term representing the component of the radiation forces associated with fluid memory effects. The viscous forces were applied to represent the quadratic damping on the floater and mooring system. However, second order wave forces are not included in this model. Mooring line dynamics: The mooring line is modeled as finite flexible elements in Riflex [14], thus nonlinear FEM mooring dynamics is used in the model. Added mass and damping are included and wave forces on the mooring line can be calculated. However the wave force on the mooring line is set to be not considered in the present calculation to better carry out the most similar comparison. Structural dynamics: The blades, tower and shaft are modeled as finite flexible elements in Riflex and the nonlinear finite element solver is employed to calculate the structural dynamics of rotor. There the gyroscopic effects and geometric stiffening effect are also considered. Generator/Transmission/Control modelling: A control model was included to enable variable speed operation maximizing power capture below the rated operation point and maintaining generator speed above the rated operation point in this simulator. Based on the optimized curve of torque versus rotational speed calculated from the aerodynamic model, a PI generator controller was employed to make the rotor keep at the specified rotational speed corresponding to different wind speeds. However, the rotational speed oscillates around the specified rotational speed because of the ever-changing aerodynamic forces. The detailed controller strategy and verification is documented in [11]. It is noted that the stiffness of the flexible blades and rotating shaft were given as large as be rigid so as to better compare the results from these two codes. Because the structural dynamics is not included in the FloVAWT while the nonlinear FEM solver is included in the Simo-Riflex-DMS code, limiting the flexibility of rotor in Riflex can reduce the uncertainty of the comparison from the flexibility aspect. FLOATING WIND TURBINE SYSTEM DEFINITION As mentioned before, the FVAWT analysed in this study is composed of the DeepWind baseline 5MW rotor [3] mounted 2 Copyright 2014 by ASME

3 on tri-column semi-submersible used within the OC4 project. The FVAWT used in this study is depicted in Figure 1. Wind Turbine Rotor The DeepWind baseline 5MW rotor is of the curved Darrieus type, as can be seen in Figure 1. The main characteristics of the rotor are given in Table 1. Mooring System The mooring system employed is identical to that used in the OC4 Phase II project [16]; three catenary lines spread symmetrically about the vertical axis, each connected to one column of the support structure (cf. Figure 1). LOAD CASES & ENVIRONMENTAL CONDITIONS Load Cases Description In this first stage of comparing FVAWT design tools, a series of load cases were devised to assess different aspects of these design tools. In the first load case (LC1) free decay tests in surge, heave, pitch and yaw were conducted to assess the natural frequencies and damping obtained by each code. In the next load case (LC2), a number of wind-only conditions are simulated to investigate the performance of aerodynamic models implemented and the effects of VAWT aerodynamic forces on platform motion. The load case following this (LC3) consisted of steady wind and regular wave simulations to assess the quasi-steady state response predicted from each code. In the last load case (LC4), a number of met-ocean conditions are simulating utilising unidirectional irregular waves with steady and turbulent winds. Tables 3 to 6 give an overview of all load cases conducted in this study. Figure 1 - Conceptual visualisation of the floating wind turbine system investigated Table 1- Main characteristics of the wind turbine rotor Rated power (MW) 5.0 Rated wind speed (m/s) 14.0 Rated rotational speed (rpm) 5.26 Rotor radius (m) Rotor height, root-to-root (m) Chord (m) 7.45 Support Structure The floating support structure utilised consists of a tricolumn semi-submersible originally designed for the OC4 project for comparing floating HAWT design tools [4] and within the DeepCwind experimental campaign for floating HAWTs [15]. The support structure is depicted in Figure 1. As described by Wang et al. [11], the ballast was rearranged to achieve the same draft as the floating HAWT configuration [16]. The main characteristics of the support structure are given in Table 2, including wind turbine inertia. Table 2 Platform main characteristics Platform mass (tonnes) Operating Draft (m) 20 Centre of gravity, from keel (m) Hydrostatic restoring stiffness, heave (kn/m) 3.836E+06 Hydrostatic restoring stiffness, roll (knm/rad) 8.080E+06 Hydrostatic restoring stiffness, pitch (knm/rad) 8.080E+06 Atmospheric & Wind Conditions For the purpose of this study the air density was fixed at 1.225kg/m 3 and the dynamic viscosity was fixed at x 10-5 kg/m s. For load cases with steady wind conditions, a vertical wind profile is applied based on the equation U i (z) = U ref ( zi z ref ) α (1) where U ref is the reference velocity at hub height, z ref is the hub height and α is the power law exponent. For this study z ref = 79.78m (vertical centre of blades) above mean sea level and α = 0.14 (according to IEC [17]). For load cases where turbulent wind conditions are specified, TurbSim [7] was used to generate turbulent wind time histories. The Kaimal turbulence wind model shall be used with a 31x31 grid resolution (TurbSim documentation suggests grid distance is not larger than blade chord [7]). Sea & Wave Conditions In this study all waves were aligned with the wind, propagating along the x-axis and the water depth was set to 200 metres. In load case 3 where regular waves are specified, they followed the Airy wave theory. In LC4 where long-crested irregular waves are specified, the JONSWAP spectral model was used. The significant wave height and peak wave period for each sub-load case are specified in Table 6. 3 Copyright 2014 by ASME

4 Table 3 - Load Case 1 Initial Condition Sim. Length (s) LC1.1 Surge m 1200 LC1.2 Heave + 6.3m 150 LC1.3 Pitch - 5 o 500 LC1.4 Yaw + 14 o 900 Table 4 - Load Case 2 Wind Condition U ref (m/s) Sim. Length (s) LC2.1 Below-rated LC2.2 Rated LC2.3 Above-rated Table 5 - Load Case 3 U ref (m/s) H (m) T (s) Sim. Length (s) LC LC LC Figure 2 - Surge free decay simulation Table 6 - Load Case 4 U ref (m/s) H s (m) T p (s) Turbulence Intensity (LC4.xT only) Sim. Length (s) LC4.1 S/T LC4.2 S/T LC4.3 S/T LC4.4 S/T LC4.5 S/T LC4.6 S/T LC4.7 S/T LC4.8 S/T Figure 3 - Heave free decay simulation RESULTS & DISCUSSION Free decay Simulations Figures 2 to 5 show the free decay responses of the FVAWT in surge, heave, pitch and yaw, respectively, as predicted by the two codes. Table 7 presents the natural periods of the FVAWT system, based on the free decay simulations by each design code. Table 7 - Natural periods obtained from free decay simulations DOF Simo-Riflex-DMS FloVAWT Surge (s) Heave (s) Pitch (s) Yaw (s) Figure 4 - Pitch free decay simulation 4 Copyright 2014 by ASME

5 As described in the Design Codes section, FloVAWT and Simo-Riflex-DMS implement different viscous damping models (in addition to radiation damping obtained through potential flow theory). FloVAWT implements a linear viscous damping model, applying a fraction of critical damping in each DOF, whilst Simo-Riflex-DMS implements a quadratic viscous damping model based on the Morison drag equation. These two different models, linear and nonlinear respectively, produce markedly different impulse response characteristics. The contributions from these models to differences in free decay simulations are seen for surge, heave, pitch and yaw in Figures 2 to 5, respectively. The successive amplitude decay predicted by the two codes follows different trends, where FloVAWT predicts larger decay than Simo- Riflex-DMS after around five oscillations (in all DOFs). initial conditions, raising and lowering of mooring lines from the sea bed would augment system inertial properties). Likewise at large displacements from the equilibrium position, mooring restoring stiffness is also augmented when using the nonlinear FEM model. This would hence also have an impact on the oscillatory frequency of the platform. In free decay responses, the platform oscillates at a damped natural frequency which is linked to the damping ratio. As mentioned before, the different damping models used would create different damping ratios, in particular the quadratic damping model, whereby a constant damping ratio may not be possible (the damping ratio is obtained from a linearised second-order system). Hence the damped natural frequency may be slightly different as predicted by the two codes. Wind-only Simulations Figures 6 to 8 illustrate the mean surge offset, mean pitch offset and mean yaw offset, respectively, for LC2 (cf. Table 5). Error bars represent the standard deviation. Figure 5 - Yaw free decay simulation The large initial displacements prescribed for each DOF may have also contributed to discrepancies. As platform velocities may be significantly large in the first few oscillations, damping in Simo-Riflex-DMS would be larger than in FloVAWT. As successive oscillatory amplitudes diminish and platform velocities significantly reduce, damping from the quadratic model will be much smaller than from the linear model (especially as velocities fall below unity). Hence the platform is allowed to oscillate with significant amplitudes for a larger number of cycles in Simo-Riflex-DMS (quadratic damping model) than in FloVAWT (linear damping model). Another discrepancy seen between the predicted free decay responses of the two codes is the oscillatory frequency of the platform, particularly in surge and pitch. This discrepancy is due to a number of reasons: FloVAWT uses a linearised force-displacement mooring line model whilst Simo-Riflex-DMS uses a nonlinear FEM model. Accounting for inertial contributions from the mooring system in real time when using the linearised force-displacement model is not trivial, and hence may lead to different system inertial characteristics as the platform is allowed to oscillate freely (e.g. at the large Figure 6 LC2 surge mean displacements with std. deviations Figure 7 LC2 pitch mean displacements with std. deviations 5 Copyright 2014 by ASME

6 In surge, pitch and yaw both design codes predicted similar trends in mean displacements, and in pitch there was very good agreement for both mean displacements and standard deviations. On the other hand, in surge and yaw there were significant discrepancies in the standard deviations predicted. Discrepancies in both mean displacements and standard deviations are mainly due to the different aerodynamic model and hydrodynamic viscous damping model implementations. Predicted aerodynamic forces, particularly at above-rated wind speeds, are sensitive to the dynamic stall models used (especially when tuning internal empirical constants), as will be discussed in following sections. As in this load case there are no wave excitation forces, platform excitation is dependent dominantly on aerodynamic forces, thereby amplifying differences in design codes when compared to more realistic environmental conditions. As can be seen in Figure 9, both codes predict similar mean pitch displacements, but significantly different standard deviations in LC3.2 and LC3.3. These significant differences are due to the different viscous damping and mooring models used in the design codes, as will be discussed in the following section. Met-ocean Simulations Steady Incident Wind Figures 10 and 11 show the mean surge offset and mean pitch offset, respectively, for LC4 with steady winds. There is good overall agreement for mean displacements throughout the range of environmental conditions simulated in LC4S. In surge (Figure 10), FloVAWT predicts slightly larger mean offset displacements than Simo-Riflex-DMS, although the same trends can be seen at below- and above-rated wind conditions. In pitch (Figure 11) there is better agreement between the two codes for mean offset displacements, although FloVAWT predicts significantly larger standard deviations at above-rated wind conditions (LC4.6S-4.8S). Figure 8 LC2 yaw mean displacements with std. deviations The different damping models implemented have a significant contribution to the predicted standard deviations. The Morison-based quadratic damping model used in Simo- Riflex-DMS gives rise to smaller standard deviations in surge and pitch, particularly in LC2.3 (above-rated wind speed), where the platform has larger relative velocities. In contrast the opposite is seen for yaw, where FloVAWT predicts smaller standard deviations in LC2.2 and LC2.3. This may be due to the different dynamic stall models used, as the predicted torque over one rotor revolution varies between models (see Wang et al. [12]). Furthermore, the turbine controller employed in Simo- Riflex-DMS results in variations in the instantaneous turbine rotational speed, which would produce a more variable torque on the platform, leading to a larger variance in platform yaw motion as compared to the constant turbine rotational speed assumed in FloVAWT. Wind-Regular Wave Simulations Figure 9 presents the mean pitch displacement for LC3, with error bars representing standard deviation. Figure 10 shows a time history for LC3.2 of the pitch motion predicted by the design codes. Figure 9 - LC3 pitch mean displacements with std. deviations Figure 10 - LC4S surge mean displacements with std. deviations 6 Copyright 2014 by ASME

7 forces. In the more severe load cases, induced relative platform velocities are considerably larger than at lower-energy load cases and hence damping forces in Simo-Riflex-DMS are larger than in FloVAWT because of the quadratic versus linear relation with relative platform velocities. Figure 11 - LC4S pitch mean displacements with std. deviations As the two design codes employ different aerodynamic dynamic stall models that rely on empirical constants, discrepancies in aerodynamic forces occur. The aerodynamic forces are highly sensitive to changes of these empirical constants, and since there is no experimental data for the rotor being studied, precise calibration of the two design codes was not possible. As the aerodynamic loads are the dominant contributor to mean offset displacements, the abovementioned issue can lead to the significant differences observed, particularly at aboverated wind speeds where dynamic stall plays a significant role. Discrepancies in mean offset displacements, particularly in surge, in severe environmental conditions (LC ) could also be attributed to the different mooring line models implemented by the design codes. Whilst FloVAWT uses a linearised force-displacement relation, Simo-Riflex-DMS uses a nonlinear FEM model considering hydrodynamic characteristics of the mooring lines. The large mean surge displacements predicted in severe environmental conditions implies that the linearised force-displacement model is not adequate in these conditions, as the mooring system stiffness would be augmented and become nonlinear. Furthermore due to the relatively high frequency of the aerodynamic forces the platform oscillates at higher frequencies, and hence hydrodynamic phenomena on mooring lines could be more significant. This would have an impact on the variance of platform motion, as the increased damping would reduce standard deviations. Whilst this is not seen to have such an impact on floating HAWTs (e.g. as discussed by [4]), due to the high-frequency oscillatory nature of VAWT forces nonlinearities in mooring systems may have a more prominent role in global platform motion performance. Another source of differences in predicted standard deviations of motion by the codes is the viscous damping models employed. FloVAWT implements a linear viscous damping model whilst Simo-Riflex-DMS a quadratic damping model. As already discussed in the free decay section, these different models can produce markedly different damping Turbulent Incident Wind Figures 12 and 13 show the mean surge offset and mean pitch offset, respectively, for LC4 with turbulent winds. In surge, FloVAWT predicts high mean and standard deviation values, which is mainly due to the different viscous damping and mooring models, as seen in steady winds. In pitch, the two design codes are in good agreement, with noticeable discrepancies at very low and very high wind speeds. As discussed earlier, the exact implementation of the aerodynamic models, in particular the way in which platform motion is considered in induction factor and velocity calculations, dynamic stall model chosen and the incorporation of turbulent wind, may be the source of these discrepancies. With regards to incorporating turbulent wind, a VAWT rotor sweeps through a three-dimensional surface and it is not trivial in time-domain simulations to convect turbulent wind velocity data and match this to instantaneous positions of the rotor. This is in contrast to a HAWT, where the rotor sweeps through a circular area in a plane. This aspect requires further detailed investigation. Figure 12 - LC4T surge mean displacements with std. deviation Another possible source of mean displacement discrepancies the control models utilised in Simo-Riflex-DMS, which would maintain the optimum rotational speed as the turbulent wind varies, maintaining aerodynamic forces that would otherwise vary by greater amounts (as in FloVAWT that assumes constant rotational velocity). Steady versus Turbulent Incident Wind Figures 14 and 15 present the mean displacements in steady and turbulent winds (LC4) for both design codes for surge and pitch, respectively. Likewise, Figures 16 and 17 7 Copyright 2014 by ASME

8 present the standard deviation of displacements in steady and turbulent winds (LC4) for both design codes for surge and pitch, respectively. The effect of turbulent wind with respect to steady wind is similar for both design codes in predicted mean displacements, which are marginally smaller in both surge and pitch for turbulent conditions. The exception is for Simo-Riflex-DMS at high wind speeds (LC ), where turbulence induced slightly larger mean pitch angles. turbulent winds induce much larger surge variability in FloVAWT than in Simo-Riflex-DMS, particularly at high wind speeds. This is due to significant influence of quadratic damping and mooring line nonlinearities at these more extreme operating conditions that are not captured by FloVAWT. Figure 15 - Mean pitch displacements in steady and turbulent winds, and irregular waves Figure 13 - LC4T pitch mean displacements with std. deviation When comparing steady to turbulent wind conditions, it can be seen that the turbulent fluctuations in incident wind can have a significant impact on overall platform motion. The control model applied in Simo-Riflex-DMS could also have had an impact on differences between predicted motion variability, as FloVAWT assumed a constant rotational speed. In Simo-Riflex-DMS, the rotational speed varies as the short-term (minutes-scale) mean turbulent wind speed varies, which would in turn vary the frequency and magnitude of aerodynamic forces. Figure 14 - Mean surge displacements in steady and turbulent winds, and irregular waves The standard deviation gives an indication of the amplitude of platform oscillations predicted by the design codes, a measure of variability of platform motion. In steady wind conditions, the more energetic the met-ocean conditions is, the larger the discrepancy between the pitch variability predicted by Simo-Riflex-DMS and FloVAWT, with significantly larger pitch variability predicted by FloVAWT. On the other hand, in all turbulent wind met-ocean conditions Simo-Riflex-DMS predicts higher pitch variability than FloVAWT. In surge, Figure 16 - Surge standard deviations in steady and turbulent winds, and irregular waves 8 Copyright 2014 by ASME

9 As there is no experimental data available, it is challenging to deduce whether the aerodynamic momentum models implemented are still valid for FVAWTs (as discussed by Borg et al. [18]), in particular when including turbulent incident wind. In the absence of experimental data, more detailed verification procedures are required using other engineering aerodynamic models (e.g. cascade, vortex models) and computational fluid dynamics (e.g. panel models, RANS). The different mooring line models used had significant effects in severe conditions where large amplitudes, particularly in surge, were predicted. The use of linearised mooring line models for such conditions might not be accurate. Different approaches in implementing aerodynamic momentum model and dynamic stall models without experimental data for calibration could be one major source of discrepancy. This has shown a major need to conducted detailed comparisons of the aerodynamic models used. In turbulent wind conditions, the two design codes were not in very good agreement, although the same trends in mean platform motion were obtained. Incorporating turbulent wind velocity data is not a trivial task considering the three-dimensional surface swept by a VAWT as well as unsteady platform motion. The differences in control models implemented in the two design codes may have contributed to differences in predicted means and standard deviations for turbulent wind conditions, particularly in pitch and surge where aerodynamic forces play a major role. Figure 17 - Pitch standard deviations in steady and turbulent winds, and irregular waves CONCLUSIONS & FUTURE WORK In this paper the first results from a preliminary code-tocode comparison for FVAWTs was presented. The two design codes involved in this study were Simo-Riflex-DMS by NTNU and FloVAWT by Cranfield University. A 5MW Darrieus-type rotor was combined with the OC4 semi-submersible support structure (as described by Wang et al. [11]) to act as the reference FVAWT system investigated in this study. A series of load cases were simulated to assess the motion performance of the FVAWT as predicted by the two codes. The results from the two codes were compared to investigate similarities and discrepancies between them. Following are the main conclusions drawn from this preliminary code verification exercise: In steady wind, met-ocean conditions both design codes were in good overall agreement in statistical platform motion, with some discrepancies in severe conditions mainly due to different damping and mooring line models utilised. The different damping models used in the two codes produced slightly different free decay responses. In metocean simulations, this had an impact on platform motion, with FloVAWT predicting larger standard deviations than Simo-Riflex-DMS. The need for appropriate viscous damping models to complement potential flow models is evident (although these may rely more on experimental data). Based on this study, a number of key aspects have been identified that will form part of further code-to-code comparisons in the near future: There is a need to thoroughly assess the aerodynamic models implemented and used for FVAWTs due to relatively more complex nature of VAWT aerodynamics as compared to HAWTs. Not only comparison of different momentum models is required but also with more complex aerodynamic models to assess their suitability for FVAWTs. Expand the analysis to consider tower base forces, mooring line forces and power production, as the current study focused solely on platform motion performance. Consider specific operational procedures such as start-up and shut-down of the turbine, survival conditions, mooring line failure and support structure flooding. Inclusion of FVAWTs design codes currently being developed by others (e.g. Cunff et al. [19] and Madsen et al. [20]) to further improve the quality of code comparison and accelerate the development of FVAWTs. ACKNOWLEDGMENTS The first and third authors would like to acknowledge that the research leading to these results has been performed in the frame of the H2OCEAN project ( and has received funding from the European Union Seventh Framework Programme (FP7/ ) under grant agreement nº It reflects only the views of the author(s) and the European Union is not liable for any use that may be made of the information contained herein. 9 Copyright 2014 by ASME

10 The second and fourth author wish to acknowledge the financial support from the Research Council of Norway through NOWITECH and the Centre for Ships and Ocean Structures at the Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway. REFERENCES [1] Jonkman, J. M. and Matha, D. (2011), "Dynamics of offshore floating wind turbines-analysis of three concepts", Wind Energy, vol. 14, no. 4, pp [2] Shires, A. (2013), "Design optimisation of an offshore vertical axis wind turbine", Proc. Inst of Civil Engineers, Energy, vol. 166, no. EN0, pp [3] Vita, L. (2011), Offshore floating vertical axis wind turbines with rotating platform (Ph.D. thesis), Technical University of Denmark, Roskilde, Denmark. [4] Robertson, A., Jonkman, J., Musial, W., Vorpahl, F. and Popko, W. (2013), "Offshore Code Comparison Collaboration, Continuation: Phase II Results of a Floating Semisubmersible Wind System", EWEA Offshore 2013, November, 2013, Frankfurt, Germany. [5] Collu, M., Borg, M., Shires, A. and Brennan, F. P. (2013), "Progress on the development of a coupled model of dynamics for floating offshore vertical axis wind turbines", Proceedings of the ASME nd International Conference on Ocean, Offshore and Arctic Engineering, 9-14 June, 2013, Nantes, France, ASME. [6] Collu, M., Borg, M., Shires, A., Rizzo, N. F. and Lupi, E. (2014), "Further progresses on the development of a coupled model of dynamics for floating offshore VAWTs", ASME 33rd International Conference on Ocean, Offshore and Arctic Engineering, 8-13 June 2014, San Francisco, USA. [7] Jonkman, B. J. (2009), TurbSim user's guide: Version [8] Fossen, T. I. and Perez, T., MSS. Marine Systems Simulator (2010), available at: (accessed ). [9] Ormberg, H., Passano, E. and Luxcey, N. (2011), "Global analysis of a floating wind turbine using an aero-hydroelastic model. Part1: Code development and case study", Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, Vol. 5, pp [10] Luxcey, N., Ormberg, H. and Passano, E. (2011), "Global analysis of a floating wind turbine using an aerohydro-elastic numerical model. Part 2: Benchmark study", Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, Vol. 5, pp [11] Wang, K., Moan, T. and Hansen, M. O. L. (2013), "A method for modeling of floating vertical axis wind turbine", Proceedings of the ASME nd International Conference on Ocean, Offshore and Arctic Engineering, 9-14 June, 2013, Nantes, France, ASME,. [12] Wang, K., Hansen, M. and Moan, T. (2013), "Model improvements for evaluating the effect of tower tilting on the aerodynamics of a vertical axis wind turbine", Wind Energy. [13] MARINTEK (2011), SIMO User's Manual. [14] MARINTEK (2009), RIFLEX User's Manual. [15] Robertson, A., Jonkman, J. M., Masciola, M., Molta, P., Goupee, A. J., Coulling, A. J., Prowell, I. and Browning, J. (2013), "Summary of Conclusions and Recommendations Drawn from the DeepCWind Scaled Floating Offshore Wind System Test Campaign", ASME nd International Conference onocean, Offshore and Arctic Engineering, 9-14 June 2013, Nantes, France. [16] Robertson, A., Jonkman, J. M., Masciola, M., Song, H., Goupee, A. J., Coulling, A. J. and Luan, C. (2012), Definition of the Semisubmersible Floating System for Phase II of OC4. [17] IEC (2007), Wind Turbines Part 3: Design Requirements for Offshore Wind Turbines,, International Electrotechnical Comm ission. [18] Borg, M., Collu, M. and Brennan, F. P. (2012), "Offshore floating vertical axis wind turbines: advantages, disadvantages, and dynamics modelling state of the art", The International Conference on Marine & Offshore Renewable Energy (M.O.R.E. 2012), September, 2012, London, RINA. [19] Le Cunff, C., Heurtier, J., Piriou, L., Perdrizet, T., Teixeira, D., Gilloteaux, J., Ferrer, G. and Berhault, C. (2013), "Fully coupled floating wind turbine simulator based on nonlinear finite element method; Part I : Methodology", Proceedings of the ASME nd International Conference on Ocean, Offshore and Arctic Engineering, 9-14 June, 2013, Nantes, France, ASME. [20] Madsen, H., Larsen, T., Vita, L. and Paulsen, U. (2013), "Implementation of the Actuator Cylinder flow model in the HAWC2 code for aeroelastic simulations on Vertical Axis Wind Turbines", 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. 10 Copyright 2014 by ASME