Startseite The effect of multi-stage modification on the performance of Savonius water turbines under the horizontal axis condition
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The effect of multi-stage modification on the performance of Savonius water turbines under the horizontal axis condition

  • Dandun Mahesa Prabowoputra , Syamsul Hadi EMAIL logo , Jung Min Sohn und Aditya Rio Prabowo
Veröffentlicht/Copyright: 12. September 2020
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Aus der Zeitschrift Open Engineering Band 10 Heft 1

Abstract

Indonesia has the abundant potential of hydropower but not yet processed optimally, which intensely depends on fossil fuel. Hydropower installed in Indonesia is only 11,272 MW, from the estimated potential approximately 94,476 MW. This data shows that 89% of the hydropower potential in Indonesia has not been processed. One of the present efforts to utilize this resource is addressed to develop the Savonius water turbine. Conventional water Savonius turbines have a comprehensible structure and easy to be applied. However, the turbines produce relatively small power, which requires further assessment to improve its performance. The current study is performed by considering geometric changes on the water Savonius turbine to observe their effect on power characteristics. Considered changes are made on the number of stages, and the angle between stages, specifically a single-stage, two-stage 0°, Two-stage 90°, three-stage 0°, and three Stage 120°. The research was carried out by designing simulation model using ANSYS software with CFX Solver. Water speed is determined to 0.8 m/s, while plates with 110 mm in diameter and 110 mm in height are incorporated as rotor configuration. Based on this study, it can be concluded that the addition of the stage affected improving the performance of the Savonius water turbine, where the multi-stage turbine experienced an increment compared to conventional water turbines. The interesting tendency was found on the two-stage rotors with angle of 0° which produced a smaller Cpmax compared to the conventional water Savonius turbines.

Keywords: Multi-Stage; Hydropower; Renewable Energy; Savonius Water Turbine; Coefficient of Power

1 Introduction

An average increase in electrical energy consumption by 4.7% and production by 11.8% in the last three years in Indonesia. Electricity consumption is dominated by the household, business, and industrial sectors. The National Power Plant is supplied by 61% of coal power plants [1]. However, the energy source has decreased every year. Another energy source that has great potential in Indonesia is Hydropower. Hydro energy and micro-mini hydro have a potential of 76% compared to other energy sources [2]. The development of a water turbine is one of the efforts to process water energy into electricity. Research on hydro turbines has been carried out such as screw-type at small hydropower plants [3]. In general, turbines are divided into horizontal axis and the vertical axis in this study using a horizontal axis Savonius type turbine. In the 1920s, Savonius published the results of carried out tests on his rotor design. They are operating in both wind and water. In the case of water, the test was carried out in the river flow, tidal waters and sea waves [4].

Research has been conducted on Savonius type turbines, especially on the vertical axis and wind turbines. Research on the effect of the overlap ratio has been carried out on vertical wind turbines. The study was conducted in an overlap ratio of 0 to 0.3. The results of the study show that the overlap ratio of 0 produces the highest Cpmax [5]. In another study regarding the overlap ratio that has been carried out on horizontal axis water turbines, it shows that Cpmax is obtained in 0.3 overlaps. The Cpmax produced in the study was 0.19 at TSR 0.79 [6]. Other research is about performance with the addition deflector variation. From this study it was found that the addition of the deflector increased the coefficient of power (Cp) by 50% [7]. The change of shape of blade becomes a helix shape, and the addition of deflector has been tested to the water Savonius turbine on the vertical axis. The study resulted in an increase in Cpmax from 0.125 to 0.14 [8]. Research on Savonius wind turbines with number of blades 2 and 3 was carried out and resulted that the Savonius wind turbines with two blades produced higher Cpmax compared to wind turbines with three blades [9]. In general, research on Savonius turbine development is about modifying the number of blades and Shape of blade which is carried out either in simulation or experiment [10]. Collaboration of the mentioned methods is very effective as results of experimental works can be expanded by numerical approach, and vice versa, which this research stage is taken as benchmarking in computational fluid dynamic [11, 12, 13, 14, 15] and finite element method [16, 17, 18, 19, 20].

This research was carried out using a Savonius type turbine on the horizontal axis. Since Savonius has a simple structure that is easy to modify and can operate at low speeds, Savonius has a lower efficiency than other types, so research still needs to be done to improve its performance [21]. This study aims to determine the effect of the number of stage on the performance of Savonius water turbines with cconsidered changes on the number of stages, precisely a single-stage, two-stage 0°, two-stage 90°, three-stage 0°, and three stage 120°.

2 Data Reduction and Model Geometry

Turbine performance always uses the relationship between the coefficient of power and the Tip Speed Ratio function. So the results of the research that has been carried out through simulation, then processed using equations to calculate the performance parameters, where TSR, Cp, and Ct is a non-dimensional number that appropriately used as a design parameter. Mathematical expressions for these terminologies are presented in Equations 1-3.

TSR (Tip Speed Ratio)

(1)TSR=ωD2U

Coefficient Power (Cp)

(2)Cp=T×ω0,5×ρ×A×U3

Coefficient Torque (Ct)

(3)Ct=CpTSR

where U is the free flow velocity, ρ is the water density, A is the Area of the Rotor, T is the torque, ω is the angular velocity, Ct is Coefficient torque and Cp is the Power coefficient [22].

This study was carried out using the Savonius rotor on the horizontal axis. The research has been done with variation single-stage, two-stage 0°, two-stage 90°, three-stage 0°, and three Stage 120°. The dimensions for each rotor are shown in Table 1. The number of rotors tested is five rotors. Description dimension on the geometry of the rotor shown in Figures 1 and 2. Figure 2 shows the top projection of single-stage rotors, two-stage 0°, and three-stage 0°. Research conducted using the aspect ratio (D /H) of 1 and the overlap ratio of 0. Table 1 shows that D is the endplate diameter, d is the rotor diameter, H is the rotor height, h is the stage height of the rotor, and T is the thickness.

Figure 1 The geometry of a) Single-stage, b) two-stage 90°, c) three-stage 120°, d) two-stage 0°, and e) three-stage 0°.
Figure 1

The geometry of a) Single-stage, b) two-stage 90°, c) three-stage 120°, d) two-stage 0°, and e) three-stage 0°.

Figure 2 Top projection of a) Single-stage, two-stage 00, and three-stage 00 b) two-stage 900, and c) three-stage 1200.
Figure 2

Top projection of a) Single-stage, two-stage 00, and three-stage 00 b) two-stage 900, and c) three-stage 1200.

Table 1

The dimension of the rotor.

ParameterRotor Type
Single StageTwo-Stage 90°Two-Stage 0°Three Stage 120°Three Stage 0°
D (mm)110110110110110
d (mm)100100100100100
H (mm)110110110110110
h (mm)-52523434
T (mm)22222

3 Methodology

The research was carried out in 3D simulation using Ansys software with cfx solver. The research was conducted with the pre-research and research stages. Performed on prestudy phase is to make 3D design, validate, mesh independence study. The second stage determines boundary conditions, meshing, and running simulations. Validation was carried out on Roy et al. [23]. Validation was carried out at the position of TSR 0.74 where Cpmax was reached. Cp at 0.65 TSR obtained from the simulation was 0.252, wherein the mentioned study, it was 0.252. The data shows a 2.35% error rate. Benchmark has shown in Figure 3.

Figure 3 Graph of benchmarking simulation with Roy’s experiment [23].
Figure 3

Graph of benchmarking simulation with Roy’s experiment [23].

Mesh independence study needs to be done in simulation research. By conducting mesh independence of our research can obtain a number of elements that are effective in the simulation process. Mesh independence study is done by adding cells [24]. Data from the mesh independence study process are shown in Figure 4 which the graph shows the relationship between the numbers of elements with a torque value. The graph shows that the most effective mesh has meshed with the number of elements 1,883,498. The number of elements in the mesh was chosen because there is no significant change in the torque value when the number of elements adds more.

Figure 4 Graph the relationship between the number of elements and torque.
Figure 4

Graph the relationship between the number of elements and torque.

4 Numerical Model

Simulations are carried out using mesh with the tetrahedral method and using inflation on the rotor wall. The results of the mesh are shown in Figures 5 and 6. The figure shows the mesh method used and mesh size. Meshing for rotary domains and domain stationaries is done separately. Figure 5 shows the mesh on the rotary domain, and Figure 6 shows the mesh on the stationary domain. In this study, CFX software has been used for computing. Using this software, all rotor designs have been analyzed.Numerical research is possibly done using numerical methods of one dimensional [25] or three-dimensional [26]. Equation of momentum, turbulent kinetic energy has been solved numerically using the software, where the governing equation is shown in Equations 4-7.

Figure 5 Mesh on a rotary domain.
Figure 5

Mesh on a rotary domain.

Figure 6 Mesh at the stationary domain.
Figure 6

Mesh at the stationary domain.

Continuity Equation:

(4)∂ρ∂t+∂(ρu)∂x+∂(ρv)∂y+∂(ρw)∂z=0

X-Momentum:

(5)∂ρu∂t+∂(ρu2)∂x+∂(ρuv)∂y+∂(ρuw)∂z=−∂ρ∂x+1Re(∂τxx∂x+∂τxy∂y+∂τxz∂z)

Y-Momentum:

(6)∂ρu∂t+∂(ρuv)∂x+∂(ρv2)∂y+∂(ρvw)∂z=−∂ρ∂y+1Re(∂τxy∂x+∂τyy∂y+∂τyz∂z)

Z-Momentum:

(7)∂ρu∂t+∂(ρuw)∂x+∂(ρvw)∂y+∂(ρv2)∂z=−∂ρ∂y+1Re(∂τxz∂x+∂τyz∂y+∂τzz∂z)

where ρ is the density, t is the time, u, v, and w are the velocities in three coordinates of Cartesian, and x, y, and z are the Cartesian coordinates.

The simulation domain consists of two domains, namely the stationary domain and the rotary domain [27]. A rotary domain is a rotating domain, and this domain consists of parts of the wall rotor, front interface, interface, and rear interface. The rotary domain is shown in Figure 7a The Stationery domain is non-rotating. This domain consists of six parts. These parts are the front interface, interface, rear interface, inlet, wall, and outlet. The stationary domain is shown in Figure 6b The rotary domain and stationary domain are connected by the interface section. The overall schematic simulation is shown in Figure 8. An inflation with a 1.2 growth rate and level of 5 was using. the small thickness of the cells in the boundary layer of the blades from the first small layer aim to smooth transition using the y ≤ 1 value highly recommended for turbulent models [28].

Figure 7 Domain of Simulation.
Figure 7

Domain of Simulation.

Figure 8 Schematic of CFD simulation.
Figure 8

Schematic of CFD simulation.

Simulation is done using water fluid. The incoming inlet flow is subsonic flow using the K-Epsilon turbulence type. The type of analysis used is the transient blade row. The inlet velocity of 0.8 m/s and the simulation is carried out at the TSR interval 0.4–1. The boundary conditions on the wall use the no-slip condition. Water flow in subsonic condition. This research is using Turbulence Model K-epsilon. For other boundary conditions shown in Table 2.

Table 2

Boundary condition of simulations

NoParameterValue
1.Fluid typeWater
2.Density (kg/m3)1,000
5.Water velocity (m/s)0.8
6.Outlet pressure (atm)1
8Gravity (m/s2)9.81

5 Results and Discussion

The performance of the Savonius hydrokinetic turbine having the variable number of stages on rotor has been investigated numerically in velocity of 0.8 m/s at TSR interval 0.4–1. From the simulation process, the pressure contours and velocity contours of each design are obtained. Also, the torque value is obtained so that Cp can be obtained from each design. Simulation has convergence at residual target 10−4. Coefficient power (Cp) and coefficient torque (Ct) generated by the Single Stage Rotor at TSR intervals of 0.3–0.9 are shown on the graph Figure 9. Cp continues to increase at TSR < 0.6 and decrease at TSR > 0.6. The resulting Cpmax is 0.1 at TSR 0.7. Ctmax is obtained at 0.2 at TSR 0.3. Where Cpmax and Ctmax are not on the same TSR.

Figure 9 Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Single-Stage.
Figure 9

Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Single-Stage.

Figure 10 shows the Cp and Ct generated by the two-stage Rotor 90°. Cp increases at TSR <0.7 then decreases at TSR > 0.7. The resulting Cpmax is 0.18 at TSR 0.8. Ct increases at TSR <0.5 Ct values increase, whereas at TSR > 0.5 Ct values decrease. Ctmax is obtained at 0.29 on TSR 0.5. The Cp and Ct produced by the two-stage 0° rotors are shown in Figure 11. The graph shows that the Cpmax is 0.058 at TSR 0.4. Ctmax was reached at 0.158 at TSR 0.3. For this rotor, the same as the previous rotor, where Cpmax and Ctmax are not on the same TSR.

Figure 10 Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Two-Stage 90°.
Figure 10

Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Two-Stage 90°.

Figure 11 Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Two-Stage 0°.
Figure 11

Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Two-Stage 0°.

Cp and Ct generated by the Rotor three Stage 120° stage at TSR intervals of 0.4-1.1 are shown in Figure 12. Cp continues to increase at TSR < 0.9 and decrease at TSR > 0.9. The resulting Cpmax was 0.197 at TSR 0.9. Ctmax was obtained at 0.249 at TSR 0.7. Where Cpmax and Ctmax are not on the same TSR.

Figure 12 Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Three Stage 120°.
Figure 12

Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Three Stage 120°.

Figure 13 shows the Cp and Ct produced by Rotor three-stage 0°. Cp increases at TSR < 0.4 then decreases at TSR > 0.4. The resulting Cpmax was 0.048 at TSR 0.4. Ct increases at TSR < 0.4 Ct values increase, whereas at TSR > 0.4 Ct values decrease. Ctmax is obtained at 0.12 at TSR 0.4. Where TSR for Cpmax is the same as TSR for Ctmax.

Figure 13 Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Three Stage 0°.
Figure 13

Graph of correlation a) Cp with TSR and b) Ct with TSR on Rotor Three Stage 0°.

Besides producing Cp and Ct values, this simulation produces output in the form of pressure contours, velocity contours, and streamlines. The pressure contours for each type of rotor are shown in Figure 14. The velocity contour is shown in Figure 15, and the velocity vector is shown in Figure 16. By studying pressure contours around models with different numbers of stage (Figures 14 and 15), there is a slight difference in pressure distribution in the blade with the same position. Where pressure is seen around the upper blade, more rotors with more stages have greater pressure. This is due to the area of the blade in multistage rotors smaller than the single-stage rotors. Figure 16 shows the vector of velocity streamlines at single-stage, two-stage, and three-stage.

Figure 14 Distribution of pressure on a) Rotor Single Stage, b)Rotor Two-Stage 0°, c) First Stage Rotor Two-Stage 90°, d) Second Stage Rotor Two-Stage 90°, e) Rotor Three Stage 0°, f) First Stage Rotor Three Stage 120°, g) Second Stage Rotor Three Stage 120°, and h) Third Stage Rotor Three Stage 120°.
Figure 14

Distribution of pressure on a) Rotor Single Stage, b)Rotor Two-Stage 0°, c) First Stage Rotor Two-Stage 90°, d) Second Stage Rotor Two-Stage 90°, e) Rotor Three Stage 0°, f) First Stage Rotor Three Stage 120°, g) Second Stage Rotor Three Stage 120°, and h) Third Stage Rotor Three Stage 120°.

Figure 15 Distribution of velocity on a) Rotor Single Stage, b)Rotor Two-Stage 0°, c) First Stage Rotor Two-Stage 90°, d) Second Stage Rotor Two-Stage 90°, e) Rotor Three Stage 0°, f) First Stage Rotor Three Stage 120°, g) Second Stage Rotor Three Stage 120°, and h) Third Stage Rotor Three Stage 120°.
Figure 15

Distribution of velocity on a) Rotor Single Stage, b)Rotor Two-Stage 0°, c) First Stage Rotor Two-Stage 90°, d) Second Stage Rotor Two-Stage 90°, e) Rotor Three Stage 0°, f) First Stage Rotor Three Stage 120°, g) Second Stage Rotor Three Stage 120°, and h) Third Stage Rotor Three Stage 120°.

Figure 16 Vector of velocity on a) Rotor Single Stage, b)Rotor Two-Stage 0°, c) First Stage Rotor Two-Stage 90°, d) Second Stage Rotor Two-Stage 90°, e) Rotor Three Stage 0°, f) First Stage Rotor Three Stage 120°,g) Second Stage Rotor Three Stage 120°, and h) Third Stage Rotor Three Stage 120°.
Figure 16

Vector of velocity on a) Rotor Single Stage, b)Rotor Two-Stage 0°, c) First Stage Rotor Two-Stage 90°, d) Second Stage Rotor Two-Stage 90°, e) Rotor Three Stage 0°, f) First Stage Rotor Three Stage 120°,g) Second Stage Rotor Three Stage 120°, and h) Third Stage Rotor Three Stage 120°.

6 Conclusions

The simulation of three different types of Savonius rotors has been carried out by involving single-stage and two-stage. The results of this study conclude that the modification of the Rotor three-stage 120° has the highest coefficient of power maximum (Cpmax) value compared to another Rotor. The Cpmax Rotor three-stage 120° is reached at 0.197, Rotor two-stage 90° is reached at 0.178, and the single-stage Cpmax is reached at 0.1. On two-stage and three-stage 0° rotors, Cpmax is lower than a single-stage, two-stage 0° is reached at 0.058 and Three-stage 0° at 0.048. Therefore, the three-stage 120° rotor is proofed as an alternative design which can improve the performance of water turbine Savonius, especially utilize water resources to produce renewable energy. From this simulation research we get a picture of the velocity and pressure distribution that can be used as a reference for experimental studies and the development of the next rotor design.

Specifications

A

swept area of the rotor

Ct

torque coefficient

Cp

power coefficient

Cpmax

power coefficient maximum

T

torque

TSR

tip speed ratio

ω

angular velocity

U

velocity

ρ

water density

x, y, z

Cartesian coordinates

Geometrical parameters of the prototype

D

endplate diameter

d

rotor diameter

H

rotor height

h

height of rotor stage

t

rotor thickness

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Received: 2020-01-04
Accepted: 2020-04-14
Published Online: 2020-09-12

© 2020 D. M. Prabowoputra et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/eng-2020-0085
Schlagwörter für diesen Artikel
Multi-Stage; Hydropower; Renewable Energy; Savonius Water Turbine; Coefficient of Power
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  30. An experimental analysis of the driver’s attention during train driving
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  32. Service quality of the urban public transport companies and sustainable city logistics
  33. Charging electric cars as a way to increase the use of energy produced from RES
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  36. Dispatching policy evaluation for transport of ready mixed concrete
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  38. New developments on EDR (Event Data Recorder) for automated vehicles
  39. General Application of Multiple Criteria Decision Making Methods for Finding the Optimal Solution in City Logistics
  40. The influence of the cargo weight and its position on the braking characteristics of light commercial vehicles
  41. Modeling the Delivery Routes Carried out by Automated Guided Vehicles when Using the Specific Mathematical Optimization Method
  42. Modelling of the system “driver - automation - autonomous vehicle - road”
  43. Limitations of the effectiveness of Weigh in Motion systems
  44. Long-term urban traffic monitoring based on wireless multi-sensor network
  45. The issue of addressing the lack of parking spaces for road freight transport in cities - a case study
  46. Simulation of the Use of the Material Handling Equipment in the Operation Process
  47. The use of simulation modelling for determining the capacity of railway lines in the Czech conditions
  48. Proposals for Using the NFC Technology in Regional Passenger Transport in the Slovak Republic
  49. Optimisation of Transport Capacity of a Railway Siding Through Construction-Reconstruction Measures
  50. Proposal of Methodology to Calculate Necessary Number of Autonomous Trucks for Trolleys and Efficiency Evaluation
  51. Special Issue: Automation in Finland
  52. 5G Based Machine Remote Operation Development Utilizing Digital Twin
  53. On-line moisture content estimation of saw dust via machine vision
  54. Data analysis of a paste thickener
  55. Programming and control for skill-based robots
  56. Using Digital Twin Technology in Engineering Education – Course Concept to Explore Benefits and Barriers
  57. Intelligent methods for root cause analysis behind the center line deviation of the steel strip
  58. Engaging Building Automation Data Visualisation Using Building Information Modelling and Progressive Web Application
  59. Real-time measurement system for determining metal concentrations in water-intensive processes
  60. A tool for finding inclusion clusters in steel SEM specimens
  61. An overview of current safety requirements for autonomous machines – review of standards
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  63. Towards online adaptation of digital twins
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  65. Fatigue Strength Analysis of S34MnV Steel by Accelerated Staircase Test
  66. The Effect of Discharge Current and Pulse-On Time on Biocompatible Zr-based BMG Sinking-EDM
  67. Dynamic characteristic of partially debonded sandwich of ferry ro-ro’s car deck: a numerical modeling
  68. Vibration-based damage identification for ship sandwich plate using finite element method
  69. Investigation of post-weld heat treatment (T6) and welding orientation on the strength of TIG-welded AL6061
  70. The effect of nozzle hole diameter of 3D printing on porosity and tensile strength parts using polylactic acid material
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  75. Analysis of reliability of compressed masonry structures
  76. Estimation of Sport Facilities by Means of Technical-Economic Indicator
  77. Integral bridge and culvert design, Designer’s experience
  78. A FEM analysis of the settlement of a tall building situated on loess subsoil
  79. Behaviour of steel sheeting connections with self-drilling screws under variable loading
  80. Resistance of plug & play N type RHS truss connections
  81. Comparison of strength and stiffness parameters of purlins with different cross-sections of profiles
  82. Bearing capacity of floating geosynthetic encased columns (GEC) determined on the basis of CPTU penetration tests
  83. The effect of the stress distribution of anchorage and stress in the textured layer on the durability of new anchorages
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  86. The Use of Statistical Functions for the Selection of Laser Texturing Parameters
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  88. Numerical Simulation of Laser Welding Dissimilar Low Carbon and Austenitic Steel Joint
  89. Assessment of Mechanical and Tribological Properties of Diamond-Like Carbon Coatings on the Ti13Nb13Zr Alloy
  90. Characteristics of selected measures of stress triaxiality near the crack tip for 145Cr6 steel - 3D issues for stationary cracks
  91. Assessment of technical risk in maintenance and improvement of a manufacturing process
  92. Experimental studies on the possibility of using a pulsed laser for spot welding of thin metallic foils
  93. Angular position control system of pneumatic artificial muscles
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  99. Technology-Integrated Project-Based Learning for Pre-Service Teacher Education: A Systematic Literature Review
  100. A Study on Water Absorption and Mechanical Properties in Epoxy-Bamboo Laminate Composite with Varying Immersion Temperatures
  101. Enhancing Students’ Ability in Learning Process of Programming Language using Adaptive Learning Systems: A Literature Review
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  103. An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network
  104. Hands-on Learning In STEM: Revisiting Educational Robotics as a Learning Style Precursor
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