Variation Number of Blades for Performance Enhancement for

Vertical Axis Current Turbine in Low Water Velocity in Indonesia

Madi, Shade Rahmawati, Mukhtasor, Dendy Satrio and Ahmad Yasim

Department of Ocean Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia

Keywords: Vertical Axis Current Turbine, Number of Blades, Performance Enhancement, Low Water Velocity.

Abstract: Asosiasi Energi Laut Indonesia (ASELI) has provided the results of the ratification of the energy potential

of ocean currents in Indonesia amounting to 17,989 MW. This amount is very large considering that

Indonesia is classified as in low water velocity. The vertical axis current turbine is proposed by many

researchers because they have lower performance than the horizontal axis current turbine. So this study

proposes research to improve the performance of the vertical axis current turbine for low water velocity by

varying the number of blades. The type of blade NACA 63

021 chose because has good performance for the

vertical axis current turbine of Darrieus type. The model of the turbine is simulated by Computational Fluid

Dynamics (CFD) with variations of the number of blades namely, 3, 4, and 5. The totals of the statistic

element of meshing are 16,090, 167,020, and 174,375 at the number of blades 3, 4, and 5, respectively. The

inlet velocity set at 1.5 m/s and tip speed ratio (TSR) are 1.5, 2, and 2.5. The final results of this study show

that the blade numbers can improve the performance of the turbine model on all TSR ranges. The blades 4

and 5 are increased by 21.8% and 15% respectively from the blades 3 on TSR 2.5. The highest performance

is obtained by the number of blades 4 on TSR 2.5 with the value of the power coefficient (Cp) 0.26.

1 INTRODUCTION

One of the technologies that are usually used for the

current energy source is the turbine. The turbine is

the main equipment in addition to the generator

(Madi et al., 2019). Generally, the turbine that is

used in the world operated in the higher current

speed, such as in Columbia 1.5-2.5 m/s (Rawlings,

2008), Italy 2 m/s (Castelli et al., 2013), China 3-4

m/s (Jing, 2014), Korea 3 m/s (Quang Le et. al,

2014), and Australia 1.5-2 m/s (Marsh et al., 2015).

Whereas several potential locations in Indonesia are

classified in the low water velocity, they can only

achieve a maximum current speed of 1.39, 1.5, and

1.79 m/s at Riau strait, Boleng strait and Mansuar

strait, respectively (Mukhtasor et al., 2014; Satrio et

al., 2018). So, the turbine which already exists in the

world, cannot be applied in Indonesia.

Asosiasi Energi Laut Indonesia (ASELI) has

provided the results of the ratification of the energy

potential of ocean currents in Indonesia amounting

to 17,989 Megawatt (Mukhtasor et al., 2014). The

amount is very abundant considering that Indonesia

is classified as in the low water velocity. Therefore,

the turbine is needed that can be applied in the low

water velocity in Indonesia.

In general, the turbine consists of two based on

its rotating axis namely, the vertical axis current

turbine (VACT) and horizontal axis current turbine

(HACT) (Khan et al., 2009; Hydrovolts, 2006 and

Duvoy et al., 2012). The VACT can respond to any

flow direction, so the results of efficiency will

stable. Whereas the HACT only responds in one

flow direction, so the results of efficiency will not

stable (Kirke and Lazauskus, 2011; Zeiner, 2015;

Bachant and Wosnik, 2015; Satrio et al., 2016).

Therefore, this study case will focus on the design of

VACT.

Particularly, the VACT consists of two namely,

Darrieus type and Savonius type (Khan et al., 2009).

The turbine of the Darrieus type is higher efficiency

than the Savonius type. However, in practice, the

efficiency is still under the HACT. It is a challenge

for researchers in this research. This study case will

try to improve efficiency the VACT of Darrieus type

in the low water velocity, by varying the number of

blades 3, 4, and 5 (Fig. 1). The blades are simulated

by Computational Fluid Dynamics (CFD).

Madi, ., Rahmawati, S., Mukhtasor, ., Satrio, D. and Yasim, A.

Variation Number of Blades for Performance Enhancement for Vertical Axis Current Turbine in Low Water Velocity in Indonesia.

DOI: 10.5220/0010047900470053

In Proceedings of the 7th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management (ISOCEEN 2019), pages 47-53

ISBN: 978-989-758-516-6

Figure 1: (a) Basic turbine, (b) variations turbine.

The type of blade NACA 63

021 is chosen for

this study because it is similar to the morphology of

humpback whales flippers (Fish and Battle, 1995).

The humpback whales are the most maneuverable of

other species and have a symmetric body so that

they will be stable and fast to catch prey (Johari et

al., 2007). The blade profile of NACA 63

021 in the

VACT of Darrieus type has a good performance

(Marsh et al., 2015). The profile of NACA 63

021

can be shown in Fig. 2.

Figure 2: The profile of NACA 63

021.

2 NUMERICAL SIMULATIONS

2.1 Turbine Geometry

Three types of turbine designs are simulated to

evaluate the influence of variations of the number of

blades. The turbine geometry (Table 1) is used based

on the published 3D CFD model data by Marsh et al

(2015). The turbine with three blades namely basic

turbine (Fig. 1a) is designed and simulated for

compared with the results of the 3D CFD model by

Table 1: Turbine Geometry.

Geometry Dimensions

Blade type NACA 63

021

Chord length (C) 0,065 m

Number of blades (N) 3, 4 and 5

Diameter of turbine (D) 0,914 m

Height of turbine (H) 0,686 m

Marsh et al (2015). After that, the variations turbine

(Fig. 1b) is designed and simulated to obtain the

influence of the performance of the turbine.

2.2 Key Performance Parameters

The performance parameters of the turbine blades

are investigated for this study namely, solidity (𝜎),

torque (𝑇), and power of the turbine (P

The solidity of the turbine is defined as the ratio

of rotor blade surface area to the frontal, swept area

that the rotor passes through (Li, 2010), where,

𝜎 





(1)

where N is the number of blades, C is chord length

(m), and R is the radius of the turbine (m).

The torque of the turbine represents the torque

coefficient. The comparison of torque of the turbine

and hydrodynamic subsystem is called by the torque

coefficient (Ct), where,

𝐶𝑡 



,   





(2)

where p is the density of the water (998.2 kg/m

), A

is the turbine swept area (HxD), and V is current

velocity (m/s).

The power of the turbine represents the power

coefficient (Cp), where,

𝐶𝑝 









(3)

where P

is the power mechanic of the turbine (watt)

and P

is the power kinetic of the water (watt).

The power of the turbine is obtained from the

value of the torque and the rotational speed, shown

in equation 4. Whereas the power kinetic is obtained

of available in the water (equation 5).

= T x ω

(4)

where ω is the rotational speed (rad/s) and T is the

torque of the turbine (N.m).

= 0,5 p A V

(5)

2.3 Boundary Conditions

The dimension of domain boundary in this study is

determined namely 10D x 20D (Fig. 3). The length

and width of domain boundary 20D and 10D

respectively were studied and recommended by

Marsh et al (2015). The domain boundary consists of

two main zones, namely, rotary, and stationary for

using the meshing method. The position of the

circular turbine is on the 5D from the inlet, 15D from

the outlet, and in the middle of the symmetry wall.

ISOCEEN 2019 - The 7th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management

The boundary conditions are simulated in the 2D

CFD model with the use of free stream conditions. In

this study case, the turbine without use arms and

shaft. The inlet and outlet conditions are set in the

velocity of 1.5 m/s and pressure 0 Pa, respectively.

The walls are set as symmetry so that the fluid has the

same distributions at the top and bottom of the walls.

Figure 3: Domain boundary of the turbine model.

2.4 Meshing

The meshing is part of designing CFD simulations, a

net strategy which is a process that determines the

final results of design studies using CFD. The use,

the number of elements in the meshing that is more

and finer will produce a better the final results, and

vice versa. However, the number of elements that

are many and smooth requires a long time. So, it

takes a meshing strategy with smooth elements and

not at the same time. In this study, the statistics of

meshing elements are used 16,090, 167,020, and

174,375 at the blades 3, 4, and 5, respectively. The

number of elements based on the criteria for grid

independence a study was conducted by Marsh et al

(2017).

In this study, the structure of meshing uses the

triangle method. The process of meshing structure in

the 2D CFD model is arranged so that the rotary

zone is made denser than the stationary zone and the

blade zone is made denser than the rotary zone. The

results of the meshing structure in this study can be

shown in Fig. 4.

Figure 4: Meshing of domain boundary the basic turbine.

The mesh density is refined (Fig. 5) in the area of

the blades, interior fluids, and interface zone by

specifying the relevance center, smoothing, span

angle center, face sizing, and edge sizing to receive

hydrodynamic flow (Marsh et al., 2015). Edge sizing

in all areas the turbine blades are the same at 0.0004

m. And then face sizing in the area of the turbine

and the interior fluids are set at 0.05 m and 0.054,

respectively.

The Inflation layers are utilized to control cell

heights near all the turbine blades wall to resolve the

boundary layer flow (Marsh et al., 2015), shown in

Fig. 6. According to recent studies, the inflation

layer set at 20 layers and the global growth rate set

at 1.2 (Satrio et al., 2018).

Figure 5: Refinement meshing of the variations turbine.

Figure 6: Inflation layers of the blade.

2.5 Solver Setup

In this study uses 6 core internal parallel processing

for simulation the turbine blades with 2D CFD. The

code is used to solved incompressible turbulence of

Unsteady Reynolds Averaged Navier Stokes (U-

RANS) equations. The k-ω SST turbulence model is

utilized because it has a good accuracy model both

free stream and boundary layer region (Marsh et al.,

2016). Beside that often is used by researchers for

vertical axis current turbines

(Dai and Lam, 2009;

Castelli et al., 2010; S Lain, 2010; Malipeddi and

Chatterjee, 2012; Marsh et al, 2012, 2013 and 2014).

The setting of the solution method at pressure

velocity uses Semi-Implicit Method for Pressure

Linked Equation (SIMPLE) for algorithm scheme

and overall is set as second-order.

Variation Number of Blades for Performance Enhancement for Vertical Axis Current Turbine in Low Water Velocity in Indonesia

In this research simulation generally uses general

transient type settings for an application to water

fluid types. General transient type settings for an

application to water fluid types. So that in the

material setting choose the type of fluid that is water

with a density of 998.2 kg/m

. Furthermore, at the

cell zone condition step, to input the rotational speed

at TSR 1.5, 2, and 2.5 are 4.92 rad/s, 6.56 rad/s, and

8.21 rad/s, respectively. The current speed data is set

at the boundary condition-stage of 1.5 m/s.

After that, residual monitor for convergence is

set at overall equation 10

-4

and the maximum of

iteration is 70. The input of calculation simulation is

the number of time step (NTS) and time step size

(TSS). In this study uses increment angle 0,9 degree

and 6 rotation of turbine at all TSR. TSS represents

the addition of angles each time the turbine rotates

and NTS represents how many turbines rotated

during the simulation (Satrio et al., 2018).

3 RESULTS AND DISCUSSION

3.1 Verification of 2D CFD Model

Verification this study of 2D CFD model simulation

is carried out against was published data of the 3D

CFD model by Marsh et al (2015). This study case

compares the basic turbine 2D CFD with the 3D

CFD model, shown in Fig. 7. The curve of Cp-TSR

shows that the result of simulation 2D CFD has a

similar trend with simulation 3D CFD. The result of

Cp in this study shows that at TSR 1.5 represents

low water velocity, different 16% with the 3D CFD

model data. Differences in the value of Cp due to the

use of different model dimensions and this study

without uses of arm and shaft model design.

Generally, at the 2D CFD model turbine by

researchers without use arm and shaft.

Figure 7: Verification 2D CFD result with the published

3D CFD data.

3.2 Effect of Blade Number on Torque

The first performance parameter in this study case is

about the torque of the turbine. Output simulation

with the 2D CFD model is data of torque coefficient

(Ct) to represent the torque of the turbine. The curve

of Ct-TSR in Fig. 8, shows the value of Ct and TSR

at the basic turbine (blade 3) and the variations

turbine (blades 4 and 5).

The value of Ct will determine the value of

torque by using equation 2. The value of the torque

at all the turbine blades is shown in Table 2.

Figure 8: Ct-TSR curve at all the turbine blades.

Table 2: The value of torque (N.m).

TSR T 3 blade T 4 blade T 5 blade

1.5 9.353 19.925 17.983

2 23.996 33.185 31.330

2.5 27.278 33.229 31.352

Based on Fig. 8 and Table 2 show that at all

TSR, the blades 4 are higher torque than the blades 3

and 5. This study uses at TSR below 3 because it

represents low water velocity. This study shows that

at all low TSR ranges the blades 4 have the best

performance with torque parameters. The torque of

the blades 4 is increased by 21.8% from the blades

3, whereas the blades 5 is increased by 15% from the

blades 3 or is decreased by 5.6% from the blades 4.

So, this study shows that performance enhancement

in the low water velocity is obtained by the turbine

blades 4, with torque parameters.

3.3 Effect of Blade Number on Solidity

The second performance parameter in this study case

is about the solidity of the turbine. The value of

turbine solidity is obtained by using equation 1. The

more the number of blades, the greater the value of

turbine solidity (σ) is shown in Table 3.

TSR

ISOCEEN 2019 - The 7th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management

Table 3: The value of solidity.

The number of blades (N) The value of solidity (σ)

3 0.07

4 0.09

5 0.11

Based on Table 3 show that the turbine blades 3,

4, and 5 have the value of solidity 0.07, 0.09, and

0.11, respectively. The effect of solidity on the

turbine performance can be shown through a curve

Cp-TSR, in Fig. 9. The power coefficient increase

with the increase in turbine solidity from the turbine

blades 3 (σ = 0.07). However, at the turbine solidity,

0.11 represents the turbine blades 5 decreases from

blades 4. The authors predict that this is due to a

loose lift force because of the turbine more solid.

The highest Cp occurs at TSR 2.5 on blades 4 with a

value of 0.26. However, the solidity 0.09 represents

the turbine blades 4 improve performance to 21.8%.

So, this study shows that performance enhancement

in the low water velocity is obtained by the turbine

blades 4, with solidity parameters (σ = 0.09).

Figure 9: Cp-TSR curve at all the turbine blades.

3.4 Effect of Blade Number on Power

The third performance parameter in this study case

is about the power of the turbine. The value of

power is determined by the rotational speed (ω) in

rad/s using equation 4. The curve of P-ω in Figure 9

shows the effect of blade number on power (P) with

the input of rotational speed namely, 4.92 rad/s, 6.56

rad/s, and 8.21 rad/s.

Based on Fig. 10 shows that the power of the

turbine is influenced by rotational speed according

to equation 4. The correlation between power output

and rotational speed are comparable. The higher the

rotational speed is the higher the power in watt at all

blade numbers. The highest power occurs at TSR 2.5

on blades 4 with a value of 273 watts. The power of

the blades 4 is increased by 21.8% from the blades

3, whereas the blades 5 is increased by 15% from the

blades 3 or is decreased by 5.6% from the blades 4.

However, the value of power at the turbine blades 4

to improve performance to 21.8% from blades 3. So,

this study case shows that performance enhancement

in the low water velocity is obtained by the turbine

blades 4, with power parameters.

Figure 10: The results of the turbine power.

4 CONCLUSIONS

The simulation of variation of the number of blades

3, 4, and 5 successfully is done. The final results of

this study case show that the number of blades can

improve the performance of the turbine model on all

low TSR ranges. The blades 4 and 5 are increased by

21.8% and 15% respectively from the blades 3 on

the TSR 2.5. The highest performance is obtained by

the number of blades 4 on TSR 2.5 with the value of

Cp 0.26. So, this study case will choose the blades 4

for further research with the experimental method.

ACKNOWLEDGEMENTS

This research is done with the assistance of several

parties. Authors thanks to the team research and

appreciation to the directorate general of resources

for science, technology, and higher education; the

ministry of research, technology, and education; the

Republic of Indonesia, which fund this research on

the scheme called, “The Thesis Magister Research”

under decree number 6/E/KPT/2019 on 02/19/19,

and contract number 5/E1/KP.PTNBH/2019 and

778/PKS/ITS/2019 on 03/29/19, and on the scheme

called, “The Basic Research” under decree number

6/E/KPT/2019 on 02/19/19, and contract number

5/E1/KP.PTNBH/2019 and 847/PKS/ITS/2019 on

03/29/19.

TSR

Variation Number of Blades for Performance Enhancement for Vertical Axis Current Turbine in Low Water Velocity in Indonesia

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Variation Number of Blades for Performance Enhancement for Vertical Axis Current Turbine in Low Water Velocity in Indonesia