Wave Energy Potential and Simulation on the Andaman Sea Coast of Thailand

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Article Wave Energy Potential and Simulation on the Andaman Sea Coast of Thailand

Chutipat Foyhirun, Duangrudee Kositgittiwong * and Chaiwat Ekkawatpanit Department of Civil Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 66, Thailand; [email protected] (C.F.); [email protected] (C.E.) * Correspondence: [email protected]

Received: 31 March 2020; Accepted: 24 April 2020; Published: 1 May 2020

Abstract: Ocean wave energy is an interesting renewable energy because it will never run out and can be available all the time. If the wave energy is to be used, then the feasibility study of localized wave potential has to be studied. This goal is to study the potential of waves in the Andaman Sea. The Simulating WAves Nearshore (SWAN) model was used to calculate the significant wave heights, which were validated with the measurement data of the Jason-2 satellite. The coastal area of Phuket and Phang Nga provinces are suitable locations for studying wave energy converters because they have high significant wave height. Moreover, this study used computational fluid dynamics (CFD) for the simulation of wave behavior in accordance with wave parameters from the SWAN model. The wave height simulated from CFD was validated with linear wave theory. The results found that it was in good agreement with linear wave theory. It can be applied for a simulation of the wave energy converter.

Keywords: Andaman Sea; computational ﬂuid dynamics; SWAN model; signiﬁcant wave height; wave energy

1. Introduction The population growth and development of both the economy and society of developing countries are main factors that aﬀect the steadily increasing energy consumption. Electricity consumption in Thailand also tends to increase according to the gross domestic product (GDP). The electricity demand between 2002 and 2014 continuously increased by an annual average rate of 4–5% [1]. In 2016, Thailand had an electricity consumption of 183 TWh, which was ranked 23rd in the world [2]. Currently, the electricity in Thailand is generated by 67% natural gas, 18% coal, 0.5% oil, 8% electricity import, 2% hydropower, and 5% renewable energy [3]. To reduce fuel consumption and electricity import, the Ministry of Energy in Thailand has prepared a long-term plan for energy use called the Thailand Integrated Energy Blueprint (TIEB), which includes the Thailand Power Development Plan (PDP), Energy Eﬃciency Development Plan (EEP), Alternative Energy Development Plan (AEDP), Natural Gas Supply Plan, and Petroleum Management Plan [4]. The main target of PDP, EEP, and AEDP is to use renewable energy to substitute natural gas for electric power generation. Ocean wave energy, which is a renewable energy source, is interesting because it is a large energy resource and because waves move almost all the time. It can generate electricity all the time. Thailand is bordered by the Gulf of Thailand and Andaman Sea. However, using ocean wave energy needs high investment. Therefore, the feasibility and potential of wave energy should be preliminarily studied. The development of technologies for electric power generation should be considered in the next step. Numerical wave models are widely used such as WAve Model (WAM), WaveWatch 3 (WW3), Simulating WAves Nearshore (SWAN) model, and MIKE21-SW. WAM and WW3 are used for oceanic scale or large area, while SWAN and MIKE21-SW are applied to shallow water and coastal or small

Sustainability 2020, 12, 3657; doi:10.3390/su12093657 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 3657 2 of 18 areas. These numerical models have different methods for action balance density equation solution with various components of source terms. The numerical model selection can be considered from the region, type of study, computational resource, etc. [5]. One of the most used numerical wave models is the SWAN model because it is a stable numerical scheme [6]. The SWAN model has been studied to evaluate wave parameters at various sea states. Akpinar et al. [6] studied the calibration and validation SWAN model in the Black Sea, and the results were compared with buoy measurements and the results of previous studies. The model parameters obtained depend on the relationship between the growth of JONSWAP (Joint North Sea Wave Project). The results of this study show agreement between observed and simulated data from the SWAN model, and the results from the SWAN model were rather better than the results from the WAM (from previous studies). Akpinar et al. [7] evaluated the potential of ocean wave energy in the Black Sea based on 15-year numerical hindcast data by using the SWAN model. The numerical setup on this study was non-stationary mode with spherical coordinates. The boundary condition was zero energy at sea state as an initial condition because the Black Sea is an enclosed area. The results of this study found that the highest significant wave height was 1.2 m and that the maximum wave power was 7 kW/m. The minimum significant wave height and wave power were 0.5 m and 0.9 kW/m, respectively. Appendini et al. [8] studied the wave energy potential in the Caribbean, which is an enclosed area, using 30 years of wave hindcast data. In this study, the wave hindcast information is validated with measured data (altimetry and buoy). The comparison results between wave information with altimetry and buoys show a satisfactory agreement. The numerical results suggest that the Caribbean Low-Level Jet is appropriate for the installation of wave power devices at a wave power of 8–14 kW/m. Wannawong et al. [9] studied the wave energy at the Gulf of Thailand in 2011 by a numerical model. This study found that the highest significant wave height was 2.6 m and that the maximum wave power was 0.485 kW/m in September at station S11. Thanathanphon et al. [10] applied the SWAN model to study wave characteristics in the Gulf of Thailand during Typhoon Muifa. These results show a good agreement with satellite data. Thanathanphon et al. [11] developed an operational wave forecast system for the Gulf of Thailand using the SWAN model. The results show that the different mean significant wave height was 0.45 m. There were satisfied agreements between the SWAN model and measured data. Wave energy converters have been continuously developed [12]. There are currently several types of wave energy converters. The wave energy converters are categorized by the location of installation and types of power take-off systems. There are three locations for the installation of the wave energy converter, which are the shoreline, nearshore, and offshore. The oscillating columns of water, underwater pneumatic systems, wave dragon systems, and oscillating bodies systems can be installed in all locations, which are based on the design of the wave energy converters [13]. Thus, these systems are presented in the form of devices that are divided by the location of installation. At the shoreline location, the wave energy converters are installed at the shore or in shallow water (10–15 m). The benefits of this location are easy installation, maintenance, and transportation of wave power [14,15]. However, this location has less wave power because of interaction with the seabed [15]. Wave energy converters. such as the Oscillating Water Column, Pendulum System, and Limpet WaveGen, are located at the shoreline. The nearshore location is positioned a few hundred meters from the shore in moderate water depth (15–25 m). The benefits of this location are similar to the shoreline location [14,15]. Wave energy converters, such as the Oyster Aquamarine, Oceanlix Energetch, Wave Star, Waveplane, and Wave, are located at the nearshore. The last type is the offshore location, where the wave energy converters are installed in deep water, which is more than 50 m, far from the shore. The wave energy converters are mostly built in floating or submerged structures moored to the seabed [15]. The disadvantages of these locations are the confusing maintenance and transportation of expensive wave power [14,15]. The advantage of these locations is high wave power [14]. Wave energy converters, such as the Wave Dragon, Pelamis wave energy converter, Power Buoy, and OE Buoy, are installed offshore. The wave energy converter in Thailand was studied by Phomkaeo and Punyasompun [16]. It was installed to test at the Sirindhorn International Environmental Park Phetchaburi and Ao Manao Narathiwat, Thailand. The wave energy converter SustainabilitySustainability2020 2020, 12, 1,2 3657, x FOR PEER REVIEW 3 of3 18 of 18

was studied by Phomkaeo and Punyasompun [16]. It was installed to test at the Sirindhorn wasInternational designed to Environmental be 1.5 m wide, 1.4Park m Phetchaburi long, and 1.5 and m high, Ao Manaoand in front, Narathiwat, it has a Thailandbuoy to floating. The wave up and downenergy the converter wave height. was designed The dimension to be 1. of5 m the wide, buoy 1. is4 m 1 mlong, wide, and 1.4 1.5 m m long, high and, and 0.2 in mfront high,, it has and a the alternatorbuoy to floating is installed up and with down the buoy the wave to generate height. electricity. The dimension The results of the foundbuoy is that 1 m the wide, generator 1.4 m long started, generatingand 0.2 m electricityhigh, and the at aalternator wave height is installed of 0.1 with to 0.6 the m. buoy The to highest generate electric electricity power. The was results 32.5 found Watts in thethat case the of generator a load of start receivinged generating 50 Watts. electricity In the caseat a wave of no height load, theof 0. electric1 to 0.6 powerm. The washighest 217.56 electric Watts. Thepower study was of wave32.5 Watts energy in converterthe case of with a load aphysical of receiving prototype 50 Watts has. In a highthe case cost. of Itno is load, necessary the electric to study thepower physical was prototype217.56 Watts with. The a numerical study of wave model energy by using converter computational with a physical fluid dynamicsprototype (CFD),has a high which . reducescost It the is necessary costs and to time study of thethe study.physical Numerical prototype Wave with a Tank numerical (NWT) model has been by using used computational for simulation of ( ) . ( ) freefluid surface dynamics waves, CFD and, which it is also reduces used the as acosts numerical and time tool of forthe studyingstudy Numerical wave energy Wave convertersTank NWT [17 ]. Wavehas been simulation used for using simulation models of oftenfree surface has wave waves reflection, and it isproblems also used as downstream a numerical oftool NWT for studying [18]. Thus, wave energy converters [17]. Wave simulation using models often has wave reflection problems for NWT, the appropriate boundary condition and method to simulate the most realistic wave before downstream of NWT [18]. Thus, for NWT, the appropriate boundary condition and method to simulating wave energy converter have to be determined. simulate the most realistic wave before simulating wave energy converter have to be determined. The theoretical wave energy in the coastal zone of Thailand was studied. The research includes The theoretical wave energy in the coastal zone of Thailand was studied. The research includes two numerical models. The significant wave height and wave energy in the coastal zone of Thailand two numerical models. The significant wave height and wave energy in the coastal zone of Thailand areare simulated simulated by by the the SWAN SWAN model. model. TheThe CFD model is is employed employed to to simulate simulate the the wave wave.. The The wave wave parametersparameters from from thethe SWAN model model are are used used to tosimulate simulate wave wavess in the in CFD the model CFD model.. The results The of results wave of wavesimulation simulation from fromCFD CFDwill be will compared be compared with significant with significant wave heights wave heights from the from SWAN the SWANmodel. model.The Theresults results of of this this study study are are the the preliminary preliminary significant significant wave wave height height study, study, possibly possibly an anappropriate appropriate locationlocation for for installing installing aa wavewave energy energy converter, an andd preliminary preliminary wave wave simulation simulation in in CFD CFD before before simulatingsimulating a a wave wave energy energy converter. converter.

2.2 The. The Study Study Area Area and and Data Data

2.1.2.1 The. The Study Study Area Area ThailandThailand is is bordered bordered by by thethe GulfGulf ofof Thailand and and the the Andaman Andaman Sea Sea.. This This study study focused focused on onthe the AndamanAndaman Sea. Sea The. The Andaman Andaman SeaSea bordersborders the south south of of Myanmar, Myanmar, west west of of Thailand, Thailand, northwest northwest of the ofthe MalayMalay Peninsula, Peninsula, north north of of Sumatra, Sumatra, andand east of the Andaman Andaman Islands, Islands, India India (Figure (Figure 1).1).

Figure 1. The Andaman Sea. Sustainability 2020, 12, 3657 4 of 18

2.2. Bathymetry Data The bathymetry data in this study were provided by the National Geophysical Data Center by the National Oceanic and Atmospheric Administration (NOAA) at Colorado, US. The bathymetry data in this study were obtained from ETOPO1 and are the reanalysis data at 1 arcminute resolution, or approximately 1.85 km. The format of ETOPO1 is used as an ASCII ﬁle which is converted by using the FORTRAN program in order that the SWAN model can read the data. The bathymetry of the Andaman Sea covers latitude 5 ◦N to 17 ◦N and longitude 90 ◦E to 99 ◦E.

2.3. Wind Data The Navy Global Environmental Model (NAVGEM) in 2014, which replaced the Navy Operational Global Atmospheric Prediction System (NOGAPS) in 2013, was used as input to the SWAN model. The wind data were collected from the 3-h data of the u and v wind components at 10 m above the water surface. The resolution of the wind data is 0.5 degrees or approximately 55 km. The NAVGEM is provided in GRIB format. This data are changed and compiled into a text format by WGIB program. It is reformatted by FORTRAN program in order that the SWAN model can read the data.

2.4. Wave Data The SWAN model was validated with the observed data, which were collected from the satellite information. This study used data from the Jason-2 satellite, which received data from NASA’s Physical Oceanography Distributed Active Archive Center. The Jason-2 satellite is a satellite that explores the characteristics of the oceans; the orbit of the Jason-2 satellite has a circular form non-sun-synchronous orbit and an approximately 10-day repeat observed in the same orbit. The Jason-2 operates using two diﬀerent radar frequencies. The Jason-2 operates using Ku-band (13.6 GHz) and C-band (5.3 GHz) frequencies. The Ku-band is the most commonly used frequency for wave model validation [19]. The observed data from the Jason-2 satellite with track numbers of 27, 90, and 166 passed through the Andaman Sea.

3. Numerical Wave Model The SWAN model was chosen for this study. The SWAN model can estimate the wave parameters in the seas, coastal areas, lakes, and estuaries from wind, bottom, and current conditions. The evolution of the wave energy is considered in predefined cells within every grid cell of size ∆x, ∆y and ∆t. The SWAN model is based on the action balance equation for shallow water as shown in Equation (1): ∂N ∂(cxN) ∂ cyN ∂(cσN) ∂(c N) S(x, y, σ, θ, t) + + + + θ = (1) ∂t ∂x ∂y ∂σ ∂θ σ where N is the action density spectrum, σ is the frequency, θ is the wave direction, and t is the time. cx and cy are the velocities in directions x and y, respectively. cσ and cθ are the propagation velocity in σ-space and θ-space, respectively. The rate of change of action in time is represented by the first term of Equation (1). The propagation of action in the x and y directions is represented by the second and third terms of Equation (1), respectively. The frequency shift and refraction due to depth and current are represented by the fourth and fifth terms of Equation (1), respectively. The source of S(x, y, σ, θ, t) represents the effects of generation, dissipation, and nonlinear wave–wave interactions. The term S(σ, θ) is term of energy density as shown in Equation (2):

S(σ, θ) = Sinp(σ, θ) + Sbrk(σ, θ) + S f rc(σ, θ) + Swcp(σ, θ) + Snl3(σ, θ) + Snl4(σ, θ) (2) where Sinp is atmospheric input, Sbrk is depth-induced wave breaking, Sfrc is bottom friction, Swcp is white-capping dissipation, Snl3 is triad nonlinear wave–wave interaction, and Snl4 is nonlinear quadruplet interactions. Sustainability 2020, 12, 3657 5 of 18

3.1. Numerical Setup in SWAN Model The SWAN cycle III version 41.01 was used for wave simulations in this study. The model was executed in non-stationary mode with Cartesian coordinates. The rectangular computational grid was executed with a grid resolution of 0.0167 and 0.0167 (1.85 km 1.85 km) in both latitude and ◦ ◦ × longitude. The direction number is 36 in the 360◦ rose, and the lowest and highest frequencies are equal to 0.042 and 0.041 Hz, respectively [20]. These numbers were used to study signiﬁcant wave height at Gulf of Thailand by using the WAM model. The spectrum shape at the computational grid boundary was the JONSWAP spectrum. The SWAN model was simulated without current in this study. The dissipation due to depth-induced wave breaking, bottom friction, and white-capping was considered in the simulation. Triad–wave interactions were also considered as nonlinear interactions for shallow water.

3.2. Wave Power

The wave energy is the combination of potential energy (Ep) due to the displacement of the water mass from mean sea level and kinetic energy due to the orbital motion of the water column. These two energy densities will be the total wave energy per unit area, which is called speciﬁc energy or energy density (E), as shown in Equation (3):

1 1 1 E = E + E = ρgH2 + ρgH2 = ρgH2 (3) p p 16 16 8 where ρ is the water density, g is the gravitational acceleration, and H is the wave height (H = 2a for a sinusoidal wave). In physical oceanography, the significant wave height is used instead of the wave height in order to use the calculation of wave energy. The significant wave height is usually defined as H = 4 √σ2 [21]. The water surface elevation time series variance for sinusoid waves s p 2 2 2 2 is σ = (1/2)a = H /8 and then Hs = 4 H /8 = 2H/ √2 and H = √2Hs/2. Substituting into Equation (3) gives the energy density in terms of the significant wave height according to Equation (4).

1 E = ρgH2 (4) 16 s

The wave energy ﬂux or wave power (P) is shown in Equation (5).

gT P = E (5) 4π

Equation (4) can be substituted by Equation (5). If the density of seawater is 1025 kg/m3, the equation for shallow water can be shown as Equation (6).

P 0.5H2T (6) ≈ s where P is the wave energy ﬂux per unit of wave-crest length (kW/m).

4. Computational Fluid Dynamics (CFD) Computational fluid dynamics (CFD) is a numerical method that can be used to solve complex fluid mechanics problems. The finite volume method (FVM) is used to solve the governing equations. In the finite volume method, the governing partial differential equations are adjusted in a conservative form and are solved for discrete control volume.

4.1. Computational Domain and Numerical Setup in CFD The results of wave characteristics from the SWAN model are used as the wave parameters in the CFD model. The results of signiﬁcant wave height from SWAN showed that the highest signiﬁcant Sustainability 2020, 12, x FOR PEER REVIEW 6 of 18

Computational fluid dynamics (CFD) is a numerical method that can be used to solve complex fluid mechanics problems. The finite volume method (FVM) is used to solve the governing equations. In the finite volume method, the governing partial differential equations are adjusted in a conservative form and are solved for discrete control volume.

4.1. Computational Domain and Numerical Setup in CFD Sustainability 2020, 12, 3657 6 of 18 The results of wave characteristics from the SWAN model are used as the wave parameters in the CFD model. The results of significant wave height from SWAN showed that the highest wave height was found in August. In this study, the wave characteristics in August were selected significant wave height was found in August. In this study, the wave characteristics in August were for the wave behavior simulation and include a wave period (T) of 6 s, wave height (H) of 3.36 m, selected for the wave behavior simulation and include a wave period (T) of 6 s, wave height (H) of and water depth (h) of 20 m. This study has wave simulation using an NWT which has the water depth 3.36 m, and water depth (h) of 20 m. This study has wave simulation using an NWT which has the as the same location as the SWAN model so the wave parameter from SWAN is also chosen for use in thewater CFD depth model. as the The same NWT location domain as in the two SWAN dimensions model canso the be seenwave in parameter Figure2, wherefrom SWAN the length is also of chosen for use in the CFD model. The NWT domain in two dimensions can be seen in Figure 2, where the NWT is 5L m, and the height (HT) of the NWT is 30 m. The wavelength (L) can be obtained from the length of the NWT is 5L m, and the height (HT) of the NWT is 30 m. The wavelength (L) can be the relationship between the wavelength, wave period, and wave height, as shown in Equation (7). Theobtained grid offrom the the domain relationship is used between in wave simulationthe wavelength, as a structure. wave period The, and grid wave in the height, wave heightas shown has in a Equation (7). The grid of the domain is used in wave simulation as a structure. The grid in the wave ﬁner grid resolution than other areas because it is the area that changes the wave according to time and position.height has The a smallfiner grid size resolution in wave than height other is used areas as because 0.001 m. it is the area that changes the wave according to time and position. The small grid size in wave height is used as 0.001 m. s 2 2 ! gT 2 4π h2 L = gT tanh 4 h (7)  2 (7) L 2π tanh T g2 2 Tg

Pressure Outlet t n Air phase I u m

e 0 O

3 Damping

SWL e a = v a W T Zone H Wave direction h Water phase W

Wall 5L (m)

Figure 2 2.. The Numerical Wave Tank (NWT)(NWT) domain.domain.

The left-sideleft-side boundary of the NWT in Figure2 2 represents represents the the inletinlet ofof thethe wave,wave, whichwhich isis defineddefined as the velocity inlet in conjunction with the Open Channel Wave BoundaryBoundary Condition (OCWBC)(OCWBC) in order toto generategenerate waves. waves The. The right right side side of theof the NWT NWT in Figure in Figure2 represents 2 represents the outlet the outlet of the wave,of the whichwave, iswhich considered is considered to be the to pressurebe the pressure outlet. outlet The top. The and top bottom and bottom of the NWTof the inNWT Figure in Figure2 are defined 2 are defined as the pressureas the pressure outlet andoutlet the and wall the with wall a with no-slip a no boundary-slip boundary condition, condition, respectively. respectively. The reflected waves typically occur at the downstream of the NWT, which is the outlet for waves. The reflected waves typically occur at the downstream of the NWT, which is the outlet for Ifwaves the reflected. If the reflected waves waves occur downstreamoccur downstream of the of NWT, the NWT, the wave the wave reflections reflections can interferecan interfere with with the incidentthe incident oncoming oncoming waves. waves The. patternsThe patterns of interference of interference depend depend on how on the how peaks the peaks and troughs and troughs of waves of arewaves matched are matche up, i.e.,d up, constructive i.e., constructive or destructive or destructive interference. interference Furthermore,. Furthermore, the reflected the reflected waves waves give inaccurategive inaccurate results. results Therefore,. Therefore, a damping a damping zone zone is definedis defined at at the the far far end end of of the the NWT NWT to to avoidavoid wave reflectionreflection atat thethe downstream, asas shownshown inin FigureFigure2 2.. This This isis completedcompleted by by addingadding aa dampingdamping sinksink termterm (S) in the momentum equation as follows: (S) in the momentum equation as follows: 1 1 S = C1ρV + C1ρ V V f (z) f (x)  (8)(8) S−  C11 V  2 C| V| V f z f x 2 z z f s f (z) = 1 − (9) − zb zzz f sfs fz  1 − (9) x zzxbs fs f (x) = − (10) xe xs − where C1 and C2 are damping coefficients, V is the velocity, and Zfs and Zb are the free-surface and bottom coordinates, respectively. Xs and Xe are the start and end positions of the damping zone in the x-direction, respectively. If the damping coefficients are defined as too much or too little damping, it could cause reflections. Thus, the damping coefficients should be appropriately defined so that the reflected waves can be completely damped. Sustainability 2020, 12, 3657 7 of 18

The volume of fluid (VOF) method is the multiphase numerical model used to solve the fluid flow problem with two or more phases [22]. For the VOF formulation, all phases are defined, and the volume occupied by one phase cannot be occupied by the others. The free surface can be identified by the volume fraction (α) concept. Only two phases are used in our case, which are water and air. In each cell, if α = 1, the cell is full of water; when α = 0, the cell is empty (contain only air); and if the value α is between 0 and 1, the cell contains the interface between water and air [23].

4.2. Linear Wave Theory The linear wave theory is used to validate with CFD results in this study. The linear wave theory can be explained by the equation that assumes that the wave is similar to a sinusoidal wave. The free surface of the wave or wave elevation are illustrated by Equation (11) as a function of time (t) and horizontal distance (x). H η(x, t) = cos(kx ωt) (11) 2 − where η is the free surface, H is the wave height, ω is the wave angular velocity, and k is the wave number.

5. Results and Discussion

5.1. Wave Comparison The results from the SWAN model were compared with Jason-2 data from February to April in 2014 in the area of the Andaman Sea. The wind data of NAVGEM have been collected since March 2013. The SWAN model was validated from February to April, which are not monsoon season in the Andaman Sea. The wind data from 2014 were used in this study. The dashed line in the map in Figure3a shows the moving line of the satellite from point 1 to point 41 on 14 February 2014. Figure3b,c shows the comparison of significant wave height results from the SWAN model and wind speed from the NAVGEM model with observed data from the Jason-2 satellite on 14 February 2014, respectively. Figure3b shows that the results of the comparison of significant wave height from the SWAN model are in good agreement with the satellite data. Figure4a shows the moving line of the satellite from point 1 to point 41 on 18 March 2014. Figure4b,c shows the comparison of significant wave height results from the SWAN model and wind speed from the NAVGEM model with the observed data from the Jason-2 satellite on 18 March 2014, respectively. The results of the comparison of significant wave height from the SWAN model and wind speed from the NAVGEM model are likely similar to the Jason-2 satellite data. Figure5a shows the moving line of the satellite from point 1 to point 41 on 17 April 2014. Figure5b,c shows the comparison of significant wave height results from the SWAN model and wind speed from the NAVGEM model with the observed data from the Jason-2 satellite on 17 April 2014, respectively. The results of significant wave height from the SWAN model and wind speed from the NAVGEM model have a similar trend with satellite data. However, the results of the comparison of significant wave height show some different results. Sustainability 2020, 12, 3657 8 of 18

Sustainability 2020, 12, x FOR PEER REVIEW 8 of 18 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 18 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 18

Figure 3. The comparison of significant wave height and wind speed between model and satellite Figure 3. The comparison of signiﬁcant wave height and wind speed between model and satellite data Figuredata at 3 . the The Andaman comparison Sea of on significant 14 February wave 2014, height 1710UTC and wind. (a speed) moving between line model of the and satellite satellite (b) at the Andaman Sea on 14 February 2014, 1710UTC. (a) moving line of the satellite (b) comparison datacomparisonFigure at 3 the. The Andaman of comparison significant Sea of wave on significant 14 height February wave results 2014, height from 1710UTC and the wind SWAN. (a )speed moving model between linewith ofmodel observed the satelliteand datasatellite (b(c)) of signiﬁcant wave height results from the SWAN model with observed data (c) comparison of wind comparisoncomparisondata at the of ofAndaman wind significant speed Sea from wave on 14the height FebruaryNAVGEM results 2014, model from 1710UTC with the observed SWAN. (a) moving model data. with line of observed the satellite data ((bc)) speed from the NAVGEM model with observed data. comparisoncomparison of of wind significant speed from wave the height NAVGEM results model from with the observed SWAN modeldata. with observed data (c) comparison of wind speed from the NAVGEM model with observed data.

Figure 4. The comparison of significant wave height and wind speed between model and satellite Figuredata at 4the. The Andaman comparison Sea on of 18 significant March 2014 wave, 2200UTC height. and(a) moving wind speed line of between the satellite model (b )and comparison satellite Figure 4. The comparison of signiﬁcant wave height and wind speed between model and satellite data datofFigure significanta at the4. TheAndaman wave comparison height Sea on results of 18 significant March from 2014 the wave ,SWAN 2200UTC height model. (anda) withmoving wind observed speed line of between datathe satellite (c) comparisonmodel (b) andcomparison ofsatellite wind at the Andaman Sea on 18 March 2014, 2200UTC. (a) moving line of the satellite (b) comparison of ofspeeddat significanta at from the Andaman the wave NAVGEM height Sea on resultsmodel 18 March fromwith 2014observedthe SWAN, 2200UTC data model.. (a )with moving observed line of data the satellite(c) comparison (b) comparison of wind signiﬁcant wave height results from the SWAN model with observed data (c) comparison of wind speedof significant from the wave NAVGEM height modelresults withfrom observed the SWAN data model. with observed data (c) comparison of wind speed fromspeed the from NAVGEM the NAVGEM model model with with observed observed data. data.

Figure 5. The comparison of signiﬁcant wave height and wind speed between model and satellite data at the Andaman Sea on 17 April 2014, 1555UTC. (a) moving line of the satellite (b) comparison of signiﬁcant wave height results from the SWAN model with observed data (c) comparison of wind speed from the NAVGEM model with observed data. Sustainability 2020, 12, 3657 9 of 18

5.2. Analysis of Data Validation In this study, the root mean square error (RMSE), bias parameter, and correlation coeﬃcient (R) are used to evaluate the accuracy of the model. The equations of RMSE, Bias, and R are Equations (12)–(14), respectively. v tu N 1 X RMSE = (P O )2 (12) N i − i i=1

N X 1 Bias = (P O ) (13) N i − i i=1 PN Pi P Oi O i=1 − − R = (14) s ! ! PN 2 PN 2 Pi P Oi O i=1 − i=1 − where Pi is the predicted value, Oi is the observed value, P and O are the average values of predicted and observed values, respectively, and N is the number of data. The significant wave height from the SWAN model was calibrated with Jason-2 satellite data in February, March, and April 2014. Figure6 shows the relation of significant wave height between the SWAN model and Jason-2 satellite data in February, March, and April 2014. The RMSE of significant wave height in February is 0.14 m and Bias = 0.02 m, which means that the significant wave height − from the SWAN model is slightly smaller than the satellite data. The R of 0.80 shows that the results from the SWAN model are in good agreement with the satellite data. The RMSE of significant wave height in March = 0.17 m and Bias = 0.08 m, which means that results from the SWAN model are − slightly smaller than the satellite data. The R of 0.56 shows that the results from the SWAN model are in good agreement with the satellite data. The RMSE of significant wave height in April is 0.21 m and Bias = 0.07 m, which means that the results from the SWAN model are slightly smaller than the − satellite data. The R of 0.68 shows that the results from the SWAN model are in good agreement with the satellite data. The terms of RMSE, Bias, and R in all 3 months indicate the good performance of the SWAN model. The validation of significant wave height from the SWAN model was executed in October, November, and December 2014. The result of validation found that RMSE is 0.13, 0.13, and 0.05 m of October, November, and December, respectively. These values of RMS illustrate that the significant wave height from the SWAN model has quite similar results with the observation data. Bias values of October, November, and December are 0.03, 0.04, and 0.02 m, respectively. They have the same − − − result with calibration, meaning that the significant wave height from the SWAN model is smaller than observation data. R values of October, November, and December are 0.83, 0.66, and 0.87, respectively. The values of R are high, indicating that the significant wave height is consistent with the observation data. Figure7 shows the relation of significant wave height between the SWAN model and Jason-2 satellite data in October, November, and December 2014. From the calibration and validation of the SWAN model, it can be applied to assess the significant wave height. Sustainability 2020, 12, x FOR PEER REVIEW 10 of 18

SustainabilitySustainability2020 2020, 12, 1,2 3657, x FOR PEER REVIEW 1010 of of 18 18

t 2.0 2.0 t h h ) ) g g i i m (a) m (b) e ( ( e

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J J 2 2

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s 0.0 0.5 1.0 1R.5=0.68 2.0 a J 0.0 SWAN Significant Wave Height (m) 0.0 0.5 1.0 1.5 2.0 SWAN Significant Wave Height (m) Figure 6. The relation of significant wave height between the Simulating WAves Nearshore (SWAN) Figuremodel 6.andThe Jason relation-2 satellite of signiﬁcant data at the wave Andaman height betweenSea in (a) the February Simulating, (b) March WAves, and Nearshore (c) April (SWAN). Figure 6. The relation of significant wave height between the Simulating WAves Nearshore (SWAN) model and Jason-2 satellite data at the Andaman Sea in (a) February, (b) March, and (c) April. model and Jason-2 satellite data at the Andaman Sea in (a) February, (b) March, and (c) April.

FigureFigure 7.7. TheThe relationrelation of of signiﬁcant significant wave wave height height between between the the SWAN SWAN model model and and Jason-2 Jason satellite-2 satellite data data at theat the Andaman Andaman Sea Sea in (ina) ( October,a) October (b), ( November,b) November and, and (c) December.(c) December. Figure 7. The relation of significant wave height between the SWAN model and Jason-2 satellite data at the Andaman Sea in (a) October, (b) November, and (c) December. 5.3. Significant Wave Height 5.3. Significant Wave Height Sustainability 2020, 12, 3657 11 of 18

5.3.Sustainability Signiﬁcant 2020, Wave 12, x FOR Height PEER REVIEW 11 of 18

ThisThis study study is is a preliminary a preliminary study; study therefore,; therefor thee, SWAN the SWAN model is model used to is determine used to determine the significant the wavesignificant height wave at the height Andaman at the Andaman Sea in 2014. Sea The in 2014 assessment. The assessment of potential of potential wave energy wave should energy study should at leaststudy 10 at years. least 10 In year thiss study,. In this the study, study the points study were points set to were evaluate set to the evaluate significant the significant wave height wave and height wave energyand wave and energy to find and a suitable to find locationa suitable for location installing for installing the wave energythe wave converter. energy converter Stations. S1 Stations to S8 are S1 locatedto S8 are nearshore located nearshore or in transitional or in transitional water zones, water where zones the, where ratio the of the ratio depth of the per depth wavelength per wavelength (H/L) is less(H/L than) is less or equal than toor 0.5, equal because to 0.5 most, because of the most technology of the fortechnology wave energy for wave converter energy was converter designed was in a transitionaldesigned in watera transitional zone. Furthermore, water zone. theFurthermore, transmission the of transmission energy from of a waveenergy energy from a converter wave energy uses lessconverter time and uses causes less time less and energy causes loss. less The energy locations loss. ofThe S1 locations to S8 in theof S1 Andaman to S8 in the Sea Andaman are as shown Sea are in Figureas shown8. in Figure 8.

Andaman Sea

S1 S2 S3 S4 S5 S6 S7 S8

Figure 8.8. Location of stations S1–S8S1–S8 in the Andaman Sea.Sea.

Figure8 8 shows shows thethe significantsignificant wavewave heightsheights ofof stationsstations S1–S8S1–S8 fromfrom JanuaryJanuary toto DecemberDecember 2014.2014. The results of significant wave height from Figure9 can be summarized as the highest and lowest The results of significant wave height from Figure 9 can be summarized as the highest and lowest significantsignificant wave height of each month, as shown in Table1 1.. TheThe highesthighest significantsignificant wavewave heightheight isis 3.363.36 m at station station S5 S5 in in August August 2014 2014.. The The lowest lowest significant significant wave wave height height is 0 is.20 0.20 m at m station at station S3 in S3 July in July2014 2014.. From From Table Table 1, the1, thehighest highest significant significant wave wave height height in each in each month month is often is often found found at station at station S4, S4,and and the the second second rank rank is atis at station station S5 S5.. The The lowest lowest significant significant wave wave height height is is usually usually at at station station S1 S1.. Stations S4S4 andand S5S5 areare the the best best locations locations of of the the eight eight locations. locations The. The nearest nearest mainland mainland shore shore to stationto station S4 isS4 Khao is Khao Lampi-Hat Lampi- TaiHat Mueang Tai Mueang National National Park (Khao Park Lampi(Khao is Lampi named is for named the park for section the park containing section thecontaining Lampi mountainthe Lampi range, mountain and Hatrange, Tai and Mueang Hat Tai is the Mueang beach is section the beach of the section park) of in the Taipark Mueang) in the District, Phang Nga Province. Hat Tai Mueang represents the natural beach forest within a national Tai Mueang District, Phang Nga Province. Hat Tai Mueang represents the natural beach forest within park, which covers the coastal area between Tap Lamru and Tai Mueang District, as shown in Figure 10. a national park, which covers the coastal area between Tap Lamru and Tai Mueang District, as shown The nearest mainland shore to station S5 is Sirinat National Park in Thalang District, Phuket Province. in Figure 10. The nearest mainland shore to station S5 is Sirinat National Park in Thalang District, The Sirinat National Park is 1 km away from Phuket International Airport. This location includes four Phuket Province. The Sirinat National Park is 1 km away from Phuket International Airport. This main beaches, which are Hat Nai Thon, Hat Nai Yang, Hat Mai Khao, and Hat Sai Kaeo. Figure 10 location includes four main beaches, which are Hat Nai Thon, Hat Nai Yang, Hat Mai Khao, and Hat shows Sirinat National Park. The shoreline of Thailand is normally a tourist destination and used for Sai Kaeo. Figure 10 shows Sirinat National Park. The shoreline of Thailand is normally a tourist fishery, ship transport, etc. For this reason, the national park is selected for installing the wave energy destination and used for fishery, ship transport, etc. For this reason, the national park is selected for converter because this area will not be disturbed from shoreline activity. Moreover, Thailand has a installing the wave energy converter because this area will not be disturbed from shoreline activity. Moreover, Thailand has a problem of coastal erosion; hence, the structure is to be built to protect from coastal erosion. The energy transportation can be difficult because it is blocked by the structure along the coast. Sustainability 2020, 12, 3657 12 of 18 problem of coastal erosion; hence, the structure is to be built to protect from coastal erosion. The energy Sustainability 2020, 12, x FOR PEER REVIEW 12 of 18 transportation can be difficult because it is blocked by the structure along the coast.

33.0.0 33.0.0 Significant Wave Height in January at station S1-S8 Significant Wave Height February at station S1-S8 ) ) m m 22.5.5 22.5.5 ( (

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00.0.0 00.0.0 00 110000 220000 330000 440000 550000 660000 770000 880000 0 110000 220000 330000 440000 550000 660000 770000 880000 Time (sec) Time (sec)

33.0.0 33.0.0 Significant Wave Height in March at station S1-S8 Significant Wave Height in April at station S1-S8 ) ) m m 22.5.5 22.5.5 ( (

t t h h g g i i

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33.0.0 33.0.0 Significant Wave Height in May at station S1-S8 Significant Wave Height in June at station S1-S8 ) )

m 22.5.5 m 22.5.5 ( (

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33.0.0 34.0.0 Significant Wave Height in July at station S1-S8 Significant Wave Height in August at station S1-S8

) ) 3.5

m 22.5.5 m 2.5 ( (

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Figure 9. Cont. Sustainability 2020, 12, x FOR PEER REVIEW 13 of 18 Sustainability 20202020,, 1212,, 3657x FOR PEER REVIEW 13 of 18

33.0.0 33.0.0 Significant Wave Height in September at station S1-S8 Significant Wave Height in October at station S1-S8 ) )

m 323.0.2.5.05 m 323.02.5..05 ( (

t Significant Wave Height in September at station S1-S8 t Significant Wave Height in October at station S1-S8 h h ) ) g g i i

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t t H H

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i 00.500.0..50 i 00.500.0..50 S 0 110000 220000 330000 440000 550000 660000 770000 880000 S 0 110000 220000 330000 440000 550000 660000 770000 880000 00.0.0 Time (sec) 00.0.0 Time (sec) 0 110000 220000 330000 440000 550000 660000 770000 880000 0 110000 220000 330000 440000 550000 660000 770000 880000 Time (sec) Time (sec)

33.0.0 33.0.0 Significant Wave Height in November at station S1-S8 Significant Wave Height in December at station S1-S8 ) )

m 323.0.2.5.05 m 323.0.2.5.05 ( (

t Significant Wave Height in November at station S1-S8 t Significant Wave Height in December at station S1-S8 h h ) ) g g i i

m 22.5.5 m 2.5 e 22.0.0 e 22.52.0.0 ( (

t t H H

h h e e g g v v i i a a e 212.0.1.5.05 e 212.0.1.5.05 H H W W

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i 00.500.0..50 i 00.500.0..50 S 00 110000 220000 330000 440000 550000 660000 770000 880000 S 00 110000 220000 330000 440000 550000 660000 770000 880000 00.0.0 Time (sec) 00.0.0 Time (sec) 00 110000 220000 330000 440000 550000 660000 770000 880000 00 110000 220000 330000 440000 550000 660000 770000 880000 Time (sec) Time (sec)

Figure 9. The significant wave heights of stations S1 to S8 in the Andaman Sea in 2014. Figure 9 9.. The significant signiﬁcant wave heights of stations S1 to S8 in the Andaman Sea in 2014.2014.

(a) (b) (a) (b) Figure 10. Location of station S4 and S5, (a) Khao Lampi–Hat Tai Mueang National Park and (b) Sirinat Figure 10. Location of station S4 and S5, (a) Khao Lampi–Hat Tai Mueang National Park and (b) National Park. FigureSirinat 10National. Location Park of. station S4 and S5, (a) Khao Lampi–Hat Tai Mueang National Park and (b) Sirinat National Park.

Sustainability 2020, 12, 3657 14 of 18

Table 1. The highest and lowest signiﬁcant wave heights from January to December 2014.

The Highest Significant Wave Height The Lowest Significant Wave Height Month Significant Wave Height (m) Station Significant Wave Height (m) Station January 2.26 S4 0.30 S1 February 1.77 S4 0.37 S1 March 1.56 S4 0.31 S1 April 1.97 S4 0.46 S1 May 2.34 S5 0.32 S1 June 2.19 S5 0.43 S1 July 1.94 S5 0.20 S3 August 3.36 S5 0.23 S2 September 2.25 S4 0.42 S1 October 2.04 S4 0.41 S1 November 2.59 S4 0.42 S2 December 2.02 S5 0.38 S1

5.4. Wave Power The highest and lowest wave powers are calculated by the highest and lowest significant wave heights of each month, respectively. The highest and lowest wave powers in 2014 are shown in Table2. The highest wave power is 10.19 kW /m in August at station S5, and the lowest wave power is 0.44 kW/m in July at station S3. It is concluded that the wave power varies with the significant wave height. The wave power of the SWAN model can be calculated by the general wave power equation, while the use of ocean wave energy is converted into electricity by a wave energy converter. The wave power from a wave energy converter may not be equal to the wave power of the SWAN model. There are many types of wave energy converters, which can be categorized by the installation location of the wave energy generator and power take-off system. Each wave energy converter is constructed for working at maximum efficiency within a specific range of wave periods and heights. The efficiency of a wave energy converter is good within this range. However, the efficiency of wave power will decrease within other ranges of wave periods and heights. Therefore, it is necessary to select a wave energy converter to suit the location, wave period, and significant wave height.

Table 2. The highest and lowest wave power from January to December 2014.

The Highest Wave Power The Lowest Wave Power Month Wave Power (kW/m) Station Wave Power (kW/m) Station January 5.54 S4 0.77 S1 February 4.35 S4 0.93 S1 March 3.49 S4 0.74 S1 April 4.08 S4 0.91 S1 May 6.08 S5 0.74 S1 June 5.26 S5 0.75 S1 July 4.54 S5 0.44 S3 August 10.19 S5 0.53 S2 September 5.40 S4 1.02 S1 October 4.63 S4 0.98 S1 November 6.60 S4 0.86 S2 December 4.73 S5 0.74 S1 Sustainability 2020, 12, 3657 15 of 18

5.5. Wave Energy Potential in Thailand Thailand’s policy is to expedite the study to determine the sources and technologies that may be applied to wave energy. It includes assessing the potential and preparing for the development of the project in Thailand. There is expectation that coastal areas of Phuket province in Andaman Sea and around Ko Samui–Ko Phangan in the Gulf of Thailand may have potential for wave energy [24]. The highest significant wave height and wave power of this study are found at station S5 in Phuket province which is the same area as expected. The significant wave height and power were high during the southwest monsoon from May to October. This study had the same result as the study of Kompor et al.[25] showing the high significant wave height and wave power during the southwest monsoon. Moreover, their results indicated that the significant wave height in the Andaman Sea is higher than that of the Gulf of Thailand. The wave energy can be generated more than it can from the Gulf of Thailand. The study in the central and southern regions of the China Sea and the studies of Zheng and Zhuang [26] and Mirzaei et al. [27] indicated that the wave energy in the Gulf of Thailand is relatively low. The Gulf of Thailand has lower significant wave height than other areas in the South China Sea due to the reduction of bathymetry and shadowing effect of the Indo-China continent. The wave power development must consider the efficiency of a wave energy converter, investment, and environment of the selected site [28]. The Pelamis, Aqua Buoy, Wave Dragon, and Oyster were used to study the possibility of wave energy converter in the South China Sea [27]. The selected site in the Gulf of Thailand was Ko Sumui. The result of the wave energy converter at Ko Sumui found that Wave Dragon had the highest average electric power than other wave energy converter. Wave Dragon, which is a wave energy converter for moderate water depth, can generate an average of 206 kW of electric power per year [27]. The wave energy potential assessment in the Caribbean Low Level Jet (CLLJ) of Appendini [8] had a value range of 8 to 14 kW/m, which is low wave energy potential. Their conclusion is that CLLJ can produce the energy. The wave energy potential in Thailand is sufficient for energy conversion, although Thailand has low wave energy potential. From the study, the Andaman Sea is more suitable than the wave potential in the Gulf of Thailand, and the Andaman Sea is satisfactory for converting wave energy. If the wave energy converters are developed, which is increasingly important in low wave energy potential areas, it will encourage the wave energy to be used more efficiently.

5.6. Wave Simulation by CFD The wave simulation with the SWAN model is a preliminary study to determine the appropriate boundary conditions of a CFD model. The wave characteristics of station S5 are simulated by using CFD. CFD is based on the Navier–Stokes equation for viscous, incompressible flow, and VOF. The wave simulation results show that wave height had a gradual dissipation of propagating waves at 36–42 s (Figure 11). The result of simulation at point 5L/2 (x) = 140 m at times of 0–42 s is compared with the linear wave theory (Figure 12). The dotted and solid lines represent the wave elevation from the CFD result and linear wave theory, respectively. The CFD result is in good agreement with linear wave theory. The result of wave height from CFD is consistent with the wave height from the SWAN model. The result had no wave reflection because the downstream is defined as a damping zone. This NWT and boundary condition is appropriate for wave simulation using CFD. Sustainability 2020,, 12,, xx FORFOR PEERPEER REVIEWREVIEW 16 of 18 Sustainability 2020, 12, 3657 16 of 18

tt = 6 s

tt = 12 s

tt = 18 s

tt = 24 s

tt = 30 s

tt = 36 s

tt = 42 s

FigureFigure 11 11... WaveWave surface at t = 6, 12, 18, 24, 30, 36, and 42 s s...

Figure 12. Surface elevation history at point x = 140 m. Figure 12 12.. Surface elevation history at point x = 140 m m..

66... Conclusions Conclusions

The results of the SWAN model were used to determine determine the wave characteristics for wave simulation andand locatelocate thethe suitable suitable location location to to study study wave wave energy energy converters converters in thein the Andaman Andaman Sea. Sea It can.. It canbe seen be seen that thethat highest the highest significant significant wave wave height height and wave and energywave energy in 2014 in are 2014 3.36 are mand 3..36 10.19 m and kW 10/m..19 at kWthe// coastalm at the area coastal of Phuket area of Province Phuket Province (at station (at S5). station Considering S5).. Considering the significant the significant wave height wave in height 2014, ininstations 2014,2014, stationsstations S4 and S5 S4S4also andand providedS5S5 alsoalso providedprovided high wave highhigh power. wavewave Thesepowerpower stations.. These stations frequently frequently showed showed the highest the highestsignificant significant wave height wave veryheight month. very month Stations.. Stations S4 and S4 S5 and are S5 the are appropriate the appropriate locations locations to study to study and andinstall install the wave the wave energy energy converter. converter From.. From these these results, results it is found,, itit isis found that Thailand that Thailand does notdoes have not nothave much not much high potential for wave energy compared tothe other area.. However,, there is still some Sustainability 2020, 12, 3657 17 of 18 high potential for wave energy compared tothe other area. However, there is still some potential of wave energy which shows that wave energy can be used as renewable energy in Thailand. The CFD model was used for wave simulation in the Andaman Sea. The wave characteristics of station S5, which is the highest significant wave height station, are used for the simulation to determine the highest wave energy from wave power generation. The downstream of this domain is designated as the pressure outlet boundary condition and has a damping zone. The results showed that wave reflection cannot be found in CFD. The wave height of the model is in good agreement with linear wave theory. Thus, the CFD result indicates that the damping scheme is quite complete for the damping, and this boundary condition is suitable for studying the performance of a wave energy converter and efficiently designing the device.

Author Contributions: Conceptualization, methodology and visualization and writing—review & editing, C.F., C.E., and D.K.; Data management and Formal analysis, C.F. and D.K.; Writing-original draft, C.F. and D.K. Supervision, C.E. and D.K. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Institutional Research Capability Development Grant from Thailand Research Fund (TRF), Thailand and King Mongkut’s University of Technology Thonburi (KMUTT), Thailand grant number IRG5980002. Acknowledgments: The authors would like to acknowledge the Institutional Research Capability Development Grant from Thailand Research Fund (TRF), Thailand and King Mongkut’s University of Technology Thonburi (KMUTT), Thailand. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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