Proceedings of the 11th European Wave and Tidal Energy Conference 6-11th Sept 2015, Nantes, France
Experimental Validation of InWave, a Numerical Design Tool for WECs Adrien Combourieu#1, Maxime Philippe#2, Alain Larivain*3, Julius Espedal$4 #
INNOSEA 1 rue de la Noë, CS12102, 44321 Nantes Cedex 03, France 1
[email protected] 2
[email protected] *
Hydrocap Energy 21, avenue Georges Pompidou, F-69486, Lyon Cedex 03 3
[email protected] $
Langlee Wave Power Hagaløkkveien 13, 1383, Asker, Akershus, Norway 4
[email protected]
Abstract— Designs of wave energy converters (WEC) are flourishing all over the world. Estimating the power extraction of these various devices is key to assess their industrial viability. To address this issue, InWave - a multibody time domain design tool dedicated to WECs - is being developed. It implements a novel approach based on relative coordinates. This article provides a validation of the program against experimental measurements on two different WEC systems: SEACAP and LANGLEE ROBUSTO. Numerical and experimental results show good agreement on these two very different systems. This study achieves the proof-of-concept of InWave. Keywords— Wave energy converter, multibody dynamics, numerical simulation, experimental validation, power production assessment
I. INTRODUCTION The amount of energy available in ocean waves is tremendous hence numerous Wave Energy Converters (WEC) projects are thriving around the world. At present, none of these projects have reached commercial scale – either because of technical or financial issues. In this context, dedicated processes and frameworks have been developed [3] to manage WEC projects. In particular, small-scale experiments coupled with numerical simulations are part of the first phase of the process. InWave was developed [1] to assist developers with numerical simulations. Among the software tools able to model WECs (WaveDyn [9], WEC-Sim [10], ProteusDS [11]), InWave is the only one based on the relative coordinates approach [12]. Therefore, a proof of concept of this novel algorithm had to be achieved. The performance of the software was first verified by comparison with system-specific programs [2]. The next step was to validate InWave against experimental results. Apart from WaveDyn (e.g. [13, 14]), there is very little experimental validation of the WEC dedicated tools. The collaborative project WEC3 [7], involving some of these programs, was recently initiated to address this issue.
ISSN 2309-1983
However, this paper presents the first series of experimental validation of InWave. First, an overview of InWave is recalled. Then, two validation test cases are considered, based on experimental results of WECs with different working principles ([4], [5]). The first test case is the commercial WEC SEACAP developed by HYDROCAP ENERGY. This system consists of a heaving buoy oscillating around a central pillar. The experimental set-up and the test campaign are briefly discussed, before the numerical modelling of the system with InWave is presented. Finally, the experimental and numerical estimates of the mean annual power extraction of this system are presented in this paper. The second test case is the LANGLEE system developed by LANGLEE WAVE POWER. This system is made of a moored semi-submersible frame and two pitching flaps. After describing both the experimental and the numerical models, this article presents and compares the motion and the absorbed power on a selection of sea states. These test cases also illustrate how scale-model experiments and numerical models can be combined to gain knowledge about WEC systems. II. OVERVIEW OF INWAVE The purpose of InWave is to model the behaviour of offshore multibody systems such as WECs. It is used for loads, motion and power assessment. The originality compared to other offshore tools lies in a fast semi-recursive multibody dynamic solver using relative coordinates [1]. It results in a reduction of degrees of freedom to be considered and therefore of the size of the system to be resolved. This is depicted in Fig. 1. This mechanical solver is coupled with a flexible linear potential flow solver [8] also using relative coordinates. Doing so, the number of elementary diffraction/radiation problems to solve is also reduced (see Fig. 2).
Copyright © European Wave and Tidal Energy Conference 2015
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Fig. 1 Relative Coordinates (left) vs. Cartesian Coordinates (right)
This innovative approach therefore decreases the CPU time, both for the hydrodynamic potential flow solver and the multibody solver.
Fig. 3 Overview of InWave Process
III. SEACAP TEST CASE The SEACAP system is developed by HYDROCAP ENERGY [4]. It consists of a heaving buoy oscillating around a central pillar. A shallow water design (up to 60m) with a fixed central pillar is studied here, but a floating design for deeper water might be later considered. The working principle of this system is shown in Fig. 4. Fig. 2 Reduction of Radiation Problems to Solve
In addition, InWave is self-contained: there is no need for an external program to use InWave. Indeed, all the relevant solvers are integrated in a series of modules that the user runs step by step (Fig. 3). After describing the multibody system based on an efficient parameterization, the user specifies the mooring model. The hydrostatic analysis is then run to find the equilibrium position of the system. A hydrodynamic database is computed calling the integrated linear potential flow solver. Once the database is established, the wave conditions are specified. The power take-off (PTO) model and other loads model are chosen and time domain simulations are run. Motion and loads time series are obtained and can be visualized. Finally, high level results are obtained by the postprocessing module. The following sections present two test cases that demonstrate the type of results that may be obtained by following this process.
Fig. 4 SEACAP System Concept
The power take-off system is harvesting the kinetic energy from the buoy’s vertical motion, which itself comes from wave excitation. A. Experimental Model An experimental campaign has been carried out by INNOSEA at DGA TH wave tank facility (France). The model’s scale was 1:22. The water depth considered in this study is 60 meters at real scale. A view of the scaled model is provided in Fig. 5.
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B. Numerical Model The SEACAP system was modelled with InWave at real scale. Therefore, all experimental results have been converted to real scale using Froude similarity. A visualisation of the InWave model is shown in Fig. 7.
Fig. 5 SEACAP Model at 1:22 Scale
Main dimensions of the system are presented in Table 1, at both real and model scale. TABLE 1 SEACAP MAIN DIMENSIONS
Radius of pillar (m) Internal radius of the float (m) External radius of the float (m) Height of the float (m) Draft of the float (m) Mass of the float (kg)
Real scale 1.50 1.92 7.5 6 3.03 286670
Fig. 7 InWave Numerical Model of SEACAP System
Model scale 0.068 0.087 0.341 0.273 0.138 26.27
The vertical position of the buoy and the force in the PTO has been recorded during the experiment. The PTO system used during the experimental campaign was a hydraulic dashpot. Its behaviour was not perfectly linear. In practice, a hysteresis loop was observed, because of a different behaviour of the dashpot when the buoy was going up or going down, as shown in Fig. 6. In addition, vortex shedding appeared due to the sharp angle of the float close to free surface. This introduced nonlinearities in the system.
To simulate the SEACAP system, the following force models were used in InWave: -
-
-
-
Nonlinear hydrostatics: it was observed during the experimental tests that the underwater volume of the buoy was highly variable. The buoy was sometimes going completely underwater or out of the water. It was thus chosen to use the instantaneous body position to compute hydrostatics. Linear Froude-Krylov: non-linear Froude-Krylov was also tested, without showing much impact on the results. Linear Froude-Krylov was therefore chosen for its lower CPU time. Linear diffraction /radiation. Nonlinear PTO: the force in the PTO was measured experimentally and plotted in function of the velocity of the buoy (see Fig. 6). This data was provided to InWave as a look-up table. During the simulation, the PTO force is deduced from the velocity by interpolation of the lookup table. Quadratic heave damping to model vortex shading due to sharp float edge.
C. Comparison Numerical vs. Experimental Comparisons on different load cases have been performed. Results are presented in the following subsections in terms of time series and RAOs.
Fig. 6 PTO Force in Function of Speed in Regular Waves (H=1.5m, T=10s)
1) Time series: Simulations of increasing complexity have been run and compared to experimental results. Initially, a simple decay test has been performed both experimentally and numerically. The float was sunk from 2.2 m and released at t=0. The PTO system was not activated. Oscillations of the system going back
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to equilibrium were measured. The results are presented in Fig. 8. This simple test is used to calibrate the value of the quadratic damping. After this calibration, comparisons show a good agreement.
Fig. 10 Force in PTO in Regular Waves
Fig. 8 Heave Decay Test
Secondly, simulations were performed in regular waves and compared with experimental measurements. The PTO system was activated. The float motion and velocity were recorded, along with the force acting in the PTO system and the absorbed power . Fig. 9 to Fig. 11 show the numerical and experimental float velocity, PTO force and absorbed power for a regular wave of H=1.5m and T=10s. They show fair agreement. Fig. 11 Absorbed Power in Regular Waves
Finally, comparisons have been performed in irregular waves. To obtain a relevant comparison with experimental results, the numerical wave time series must be the same as the one measured in the basin. The measured wave signal is used as an input for InWave which computes a frequency spectrum from it. The wave elevation is then reconstructed in time domain during simulation. This is shown in Fig. 12.
Fig. 9 Float Velocity in Regular Waves
Nonlinearities of the system can clearly be observed in these time series as the responses are not pure sinusoids. For example, the “step” observed on float velocity corresponds to the float reaching the top of its trajectory and therefore a change in the PTO regime which goes from being pushed to being pulled. The float is slightly blocked when this happens (also visible in Fig 4.). This phenomenon is captured by InWave due to the fact that a non-linear PTO behaviour was inputted through an experimental look-up table. Fig. 12 Free Surface Reconstruction from Measurements (Hs=3.5m, Tp=10s)
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Fig. 13 to Fig. 15 show the numerical and experimental float velocity, PTO force and absorbed power for an irregular wave of Hs=3.5m and Tp=10s. A fair agreement is found. Deviations have different sources. First, the viscous drag due to sharp edges actually depends on the amplitude of motion and therefore the quadratic drag coefficient is not constant. In addition, the PTO non-linear behaviour is actually also depending on wave amplitude. The errors are of course more visible in Fig. 13 as they are squared here.
2) Response Amplitude Operator: The first order response of the system is extracted from time series in regular waves by harmonic analysis. This response is then normalized by the wave amplitude resulting in “pseudo-RAOs” obtained in time domain. Numerical simulations in regular waves were launched with varying wave amplitude corresponding to the experimental wave amplitude. This was performed both with and without PTO activated. A fair agreement was found in both cases as shown by Fig. 16 and Fig. 17. Motion appears to be overestimated, probably because of underestimation of energy loss due to vortex shedding and friction in the PTO.
Fig. 13 Float Velocity in Irregular Waves
Fig. 16 Pseudo RAO of Vertical Motion without PTO
Fig. 14 Force in PTO in Irregular Waves
Fig. 17 Pseudo RAO of Vertical Motion with PTO
IV. LANGLEE TEST CASE LANGLEE WAVE POWER is developing a wave energy converter system called LANGLEE Robusto. This system is a semi-submersible floating installation comprised of a moored base with two pitch oscillating wings. The wave mechanical energy is extracted by generators from the two wings’ angular motion. It is floating at the water surface but most of the unit Fig. 15 Absorbed Power in Irregular Waves
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is submerged. Four mooring lines keep it in position. Normal installation site is at 40-100 meters water depth.
Fig. 18 LANGLEE Robusto System Concept
A. Experimental Model LANGLEE system has been tested during a test campaign at CEHIPAR facility (Spain). The scale of the experimental model was 1:12. A view of the scaled model is displayed in Fig. 19.
Fig. 20 Torque in PTO in Function of Wing Angle
B. Numerical Model The LANGLEE system was modelled with InWave, under ICEX/INVEST IN SPAIN and ERDF funding framework. It was modelled at real scale. Therefore, all experimental results have been converted to real scale using Froude similarity. A visualisation of InWave model is shown in Fig. 21.
Fig. 19 Scaled Model of the LANGLEE System
Main dimensions of the system are given in Table 2.
Fig. 21 InWave Numerical Model of LANGLEE System
To simulate this system, the following force models were used in InWave:
TABLE 2 MAIN DIMENSIONS OF LANGLEE SYSTEM
Model scale Base
Lpp (m)
2,6
31,2
Bpp (m)
1,35
16,2
0,1
1,2
Diameter (m) Wing
Real scale
Bpp (m) H (m)
1
12
0,729
8,748
Motions of the base, wings angle and torque acting in the PTO have been measured during the experiment. Electrical generators were used to simulate the PTO. The behaviour of the PTO was close to linear (Fig. 20). Finally, the system was tightly moored by 4 chains attached at each corner ensuring a small motion of the base. The mooring was designed according to DNV-GL standards and the mooring forces measured during the campaign were within the acceptable limits, even for extreme wave conditions.
-
Nonlinear hydrostatics. Linear Froude-Krylov. Linear diffraction /radiation. Linear PTO: a linear damping value obtained by interpolation of the experimental speed/torque relation. Viscous drag on the wings was modelled using Morison formulation with relative velocity [6].
In addition, as the experimental mooring system was tight, the base motion was in practice very small. Therefore, the only degrees of freedom considered in the numerical model were the rotations of the 2 wings. C. Comparison Numerical vs. Experimental Comparisons have been carried out on a set of irregular sea states. Wing velocity, torque in PTO and absorbed power are compared in term of time series and statistics. They are shown without dimensions due to confidentiality matters. 1) Time series: To perform a relevant comparison of time series in irregular seas, the experimental wave time series are imported into InWave. Hence the
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numerical model will experience the same waves as the experimental model (see Fig. 22).
2) Statistics: The instantaneous absorbed power is derived from the wing velocity and torque time series. Standard deviation of wing velocity and torque in PTO are calculated for each sea state, as well as average absorbed power. The sea states that have been considered are listed in Table 3. TABLE 3 SEA STATES USED FOR C OMPARISONS OF LANGLEE MODELS
Hs (m)
Fig. 22 Wave Elevation Time Series for Hs=1.5m and Tp=5s
Simulations have been run in irregular waves over 1000s with a time step of 0.1s. Fig. 23 shows the comparison of the fore wing velocity between the experimental and the numerical models for Hs=1.5m and Tp=5s.
Tp (s) 1
6
1.5
10
2
12
3.5
10
1
7
1.5
5
1.5
7
1
10
1
12
These statistical results are presented in Fig. 25 to Fig. 27. These figures show that the behaviour of the numerical model is in fair agreement with the experimental model of the LANGLEE system. It proves that InWave is able to model a complex system with hydrodynamic interactions and nonCartesian degrees of freedom.
Fig. 23 Fore Wing Velocity Time Series for Hs=1.5m and Tp=5s
Fig. 24 shows the comparison of the fore wing torque in the same sea state. These time series show that the numerical model matches the experimental model fairly well on one sea state example. The next step is to prove that the overall behaviour of the numerical model is satisfactory.
Fig. 25 Fore Wing Velocity Standard Deviation Over all Sea States
Fig. 24 Fore Wing Torque in PTO Time Series for Hs=1.5m and Tp=5s
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[3]
[4] [5] [6] [7]
[8] [9] Fig. 26 Fore Wing Torque in PTO Standard Deviation Over all Sea States
[10] [11] [12]
[13]
[14]
Wave Energy Converters from NumWEC Project,” 2nd Asian Wave and Tidal Energy Conference, 2014, Tokyo. J. Weber, “WEC Technology Readiness and Performance Matrix – finding the best research technology development trajectory,” 4th International Conference on Ocean Energy, 2012, Dublin. HYDROCAP ENERGY website: http://www.hydrocap.com/ LANGLEE WAVE POWER website: http://www.langleewavepower.com/ Veritas, Det Norske. "DNV-OS-J101." Design of offshore wind turbine structures (2011). A. Combourieu, M. Lawson, A. Babarit, K. Ruehl, A. Roy, D. Steinke, R. Costello, M. Livingstone, P. Laporte Weywada, “WEC3: Wave Energy Converters modeling Code Comparison project,” 11th European Wave and Tidal Energy Conference, 2015, Nantes. NEMOH on LHEEA lab. website from Ecole Centrale de Nantes : http://lheea.ec-nantes.fr/doku.php/emo/nemoh/start WaveDyn by DNVGL : http://www.gl-garradhassan.com/en/software/25900.php WEC-Sim by NREL and SNL: http://en.openei.org/wiki/WEC-Sim ProteusDS by DSA: http://dsa-ltd.ca/software/proteusds/description/ F. Rongère, A. Clément. “Systematic Dynamic Modeling and Simulation of Multibody Offshore Structures: Application to Wave Energy Converters”, OMAE2013-11370, Nantes, France. J. Lucas, M. Livingstone, M. Vuorinen and J. Cruz. “Development of a wave energy converter (WEC) design tool – application to the WaveRoller WEC including validation of numerical estimates”, 4th International Conference on Ocean Energy, 17 October, Dublin. Joao Cruz, Ed Mackay, Michael Livingstone and Benjamin Child. “Validation of design and planning tools for wave energy converters”, Proceedings of the 1st Marine Energy Technology Symposium, METS13, April 10-11, 2013, Washington, D.C.
Fig. 27 Total Average Absorbed Power (Both Wings) Over all Sea States
V. CONCLUSIONS This validation with two different WEC systems has shown the capability of InWave to accurately model the dynamic behavior of systems with different working principles. This series of comparisons establishes the proof of concept of InWave’s algorithms based on recursive multi body algorithm and linear potential flow theory. Benchmarking of InWave against other WEC-dedicated programs is on-going [7]. ACKNOWLEDGMENT The author would like to thank Hydrocap Energy and Langlee Wave Power for allowing publishing this study on their patented systems. Staffs of the wave tank facilities from DGA TH and CEHIPAR are also acknowledged for their precious help and availability. REFERENCES [1]
[2]
A. Combourieu, M. Philippe, F. Rongère, A. Babarit, “InWave: A New Flexible Design Tool Dedicated to Wave Energy Converters,” 33rd International Conference on Ocean, Offshore and Artic Engineering, 2014, San Francisco. V. Leroy, A. Combourieu, M. Philippe, F. Rongère, A. Babarit, “Benchmarking of the New Design Tool InWave on a Selection of
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