Optimization of wind Turbine blade design using wind tunnel testing

Optimization of wind turbine blade
designs using wind tunnel testing
Rab-Nawaz Ahmed (GL)
Usama (AGL)
Najeeb Soomro (GM)
Farhan Ali Bhatti (GM)
Presenting by Group 5th
(21ESE19)
(21ESE44)
(21ESE18)
(21ESE39)
Supervisor
Prof. Dr. Shahid Hussain Siyal
QUAID-E-AWAM UNIVERSITY
OF ENGINEERING, SCIENCE & TECHNOLOGY
NAWABSHAH, SINDH, PAKISTAN
ENERGY SYSTEMS ENGINEERING DEPARTMENT

Table of contents
Introduction
Literature Review
Problem Statement
Aim and Objectives
Methodology
Possible outcome
References

Introduction
 Wind energy is a clean, renewable, and sustainable alternative to
fossil fuels, reducing greenhouse gas emissions[1].
 Unlike fossil fuels, wind power does not deplete natural
resources and has minimal environmental impact[2].
 Wind energy is one of the fastest-growing energy sources
globally, with installed capacity increasing annually[3].
Role of Wind Turbines in Energy Production:
 Wind turbines convert kinetic energy from wind into electrical
energy using aerodynamically designed blades.
 The efficiency of wind turbines depends on multiple factors,
including blade design, material, and wind conditions[4].

Introduction Cont...
Need for Blade Optimization:
 The performance of a wind turbine is largely determined by the
shape, angle, and material of its blades[5].
 Traditional blade materials (such as fiberglass and carbon fiber)
are expensive and complex to manufacture[6].
 Wood and PVC have emerged as potential low-cost and
sustainable alternatives, but their aerodynamic performance
requires further investigation[7].
Importance of Wind Tunnel Testing:
 Wind tunnel testing allows controlled evaluation of blade
performance under various wind speeds and angles of attack[8].
 Experimental testing provides real-world validation of theoretical
aerodynamic models, making it a crucial step in blade design
optimization[10].

Literature Review
Sr.# (Author, Year)
Ref.
Objective Findings Limitations
1
Sathish, 2024
[11]
Study
aerodynamics of
turbine
cascades
Provided data for
CFD validation
and aerodynamic
behavior analysis.
Material details not
provided; limited to
subsonic
applications.
2 Nabhani et al.,
2024
[12]
Improve
efficiency using
synthetic jets
Enhanced
efficiency of large-
scale horizontal
axis turbines.
Limited experimental
setup; computational
validation needed.
3
Zhao et al.,
2024
[13]
Assess
aerodynamic
characteristics
of different blade
structures
Measured
rotational speed,
lift, drag, and
torque variations.
Material specifics not
mentioned.

Literature Review Cont…
Sr.# (Author, Year)
Ref.
4
Sarmast et al.,
2024
[14]
Improve wind
farm efficiency
with pitch
control
Tested individual
pitch control
strategy (Helix) to
enhance wake
mixing.
Focused on control
strategy, not material
properties.
5 Zahle et al.,
2023
[15]
Optimize turbine
derating
strategies
Proposed blade-
pitch actuation
regulator for
control efficiency.
provided.
6
van der Hoek
et al., 2023
[16]
Improve wake
recovery using
pitch control
Increased
combined power
output by 15%
using Helix pitch
control.
Study limited to a
two-turbine setup.

Literature Review Cont…
Sr.# (Author, Year)
Ref.
7 Hosseini et al.,
2023
[17]
Optimize
Savonius wind
turbine
performance
Improved torque
coefficient (13.74%),
rotational speed
(0.071%), and
power coefficient
(5.32%).
Based on simulations;
experimental validation
needed.
8
Guma & Nishino,
2022
[18]
Analyze wake
characteristics of
optimized blades
Clarified influence of
dynamic similarity
on wake modeling.
provided.
9 Barlas et al.,
2021
[19]
Optimize
aerodynamic
performance of
blade tip shapes
Improved power
performance
through curved tip
shape design.
Focused on modeling,
not real-world
applications.

Problem Statement
 Wind turbine performance depends on blade design, but there is
not enough real testing on different blade shapes and materials.
 Most studies use computer simulations, which may not fully
match real-world performance.
 The impact of materials like wood and PVC on turbine efficiency
(speed, lift, drag, and torque) has not been properly tested.
 Better blade designs are needed, but there is not enough
experimental data to improve them effectively.
 There is a gap between theory and real performance, making it
hard to create the best wind turbine blades.

Aim and Objectives
Aim
This study aims to refine wind turbine blade aerodynamics through
wind tunnel experiments. By analyzing different blade shapes and
configurations, we seek to improve turbine efficiency and contribute
to the future of wind energy technology.
Objectives
 To study how different blade shapes affect wind turbine performance
using wind tunnel tests.
 To test how materials like wood and PVC impact turbine efficiency,
including speed.
 To compare the performance of different blade designs based on
real experimental data.
 To improve blade designs for better energy output and efficiency.

Methodology
Step 1: Designing the Blades
 Create different blade shapes and sizes to test how they affect
performance.
 Choose wood and PVC(polyvinyl Chloride) as materials to
compare their durability, weight, and efficiency.
 Adjust blade angles and airfoil shapes to improve wind capture.
Step 2: Fabricating the Blades
 Cut and shape the blades carefully using hand tools.
 Smooth and finish the surfaces to reduce air resistance and
improve aerodynamics.
 Ensure the blades are balanced and securely attached to the
rotor.

Methodology
Step 3: Setting Up the Wind Tunnel
 Mount the wind turbine model inside a controlled wind tunnel.
 Adjust the wind speed using a computer-controlled axial fan.
 Connect sensors and a SCADA(Supervisory Control and Data
Acquisition) system to monitor performance in real-time.
Step 4: Running the Tests & Collecting Data
Test each blade design under different wind speeds.
Measure key factors like:
 Wind speed.
 Rotational speed (RPM).
 Voltage & current.
 Thrust force & mechanical torque
 Record and save all the data for analysis.

Methodology
Step 5: Analyzing Performance
 Could you find out which blade design produces the most power
efficiently?
 Identify the best material, shape, and angle combination for
maximum wind energy conversion.
Step 6: Optimization & Recommendations
 Conclude which blade design is the best for efficiency and
durability.
 Suggest possible improvements, like modifying blade angles or
testing new materials.
 Recommend further research, such as using CFD simulations or
experimenting with stronger lightweight materials.

Possible Outcomes
 Identification of the most efficient blade shape based on
power output.
 Comparison of wood vs. PVC blades in terms of efficiency
and cost-effectiveness.
 Evaluation of the impact of blade angle and number of blades
on performance.
 Analysis of wind speed variations and their effect on turbine
output.
 Measurement of thrust force and mechanical torque to assess
turbine stability.

[1] Sundar, P., et al. (2021). Advances in Wind Energy Technology. Renewable Energy Journal, 45(3), 210-
225.
[2]Manwell, J. F., McGowan, J. G., & Rogers, A. L. (2010). Wind Energy Explained: Theory, Design, and
Application. John Wiley & Sons.
[3] Global Wind Energy Council. (2023). Global Wind Report 2023.
[4] Burton, T., Sharpe, D., Jenkins, N., & Bossanyi, E. (2020). Wind Energy Handbook. Wiley.
[5] Sharma, R., & Gupta, A. (2022). "Aerodynamic Performance of Wind Turbine Blades: A Review," Energy
Science Journal, 39(2), 112-130.
[6] Kumar, M., et al. (2019). Sustainable Wind Turbine Materials: Challenges and Prospects. Materials Today:
Proceedings.
[7] Ali, S., et al. (2023). "Comparative Study of PVC and Wooden Wind Turbine Blades in Experimental
Conditions," Renewable Energy Research, 18(4), 178-193.
[8] Shaikh, F., et al. (2022). "Wind Energy Potential in Pakistan: A Review," Renewable Energy & Sustainability
Journal, 15(2), 98-110.
[9] Anderson, J. D. (2017). Fundamentals of Aerodynamics. McGraw-Hill.
[10] Tang, X., et al. (2021). "Experimental Validation of Wind Turbine Blade Performance Using Wind Tunnel
Testing," Journal of Applied Fluid Mechanics, 14(1), 89-102.
[11] Sathish, S. (2024). Wind tunnel testing and modeling implications of an advanced turbine cascade. arXiv
preprint arXiv:2407.11210. https://arxiv.org/abs/2407.11210
[12] Nabhani, A., Tousi, N. M., Coma, M., Bugeda, G., & Bergada, J. M. (2024). Large-scale horizontal axis
wind turbine aerodynamic efficiency optimization using active flow control and synthetic jets. arXiv preprint
arXiv:2407.20746. https://arxiv.org/abs/2407.20746
References

[13] Zhao, X., Zhang, Z., & Wang, J. (2024). Wind tunnel experimental study on the aerodynamic
characteristics of wind turbines with different structural parameters. Philosophical Magazine.
https://www.tandfonline.com/doi/full/10.1080/14786451.2024.2305035
[14] Sarmast, S., Ivanell, S., & Mikkelsen, R. (2024). Wind tunnel investigations of an individual pitch control
strategy for wind farm flow control. Wind Energy Science, 9, 1251–1265.
https://wes.copernicus.org/articles/9/1251/2024/
[15] Zahle, F., Sørensen, N. N., & Johansen, J. (2023). Wind tunnel testing of wind turbine and wind farm
control strategies. Journal of Renewable and Sustainable Energy, 16(5), 053302.
https://pubs.aip.org/aip/jrse/article/16/5/053302/3311727
[16] van der Hoek, D., Schepers, G., & van Kuik, G. (2023). Individual blade pitch control for wake recovery
in wind farms: A wind tunnel study. Renewable Energy, 210, 180-191. https://arxiv.org/abs/2306.12849
[17] Hosseini, S. E., Karimi, O., & AsemanBakhsh, M. A. (2023). Multi-objective optimization of Savonius
wind turbine. arXiv preprint arXiv:2308.14648. https://arxiv.org/abs/2308.14648
[18] Guma, D., & Nishino, T. (2022). Wind tunnel tests of wake characteristics for a scaled wind turbine
model with thrust-optimized blades. Energies, 15(17), 6165.
https://www.mdpi.com/1996-1073/15/17/6165
[19] Barlas, T., Madsen, H. A., & Zahle, F. (2021). Aerodynamic optimization of curved tip shapes for wind
turbine blades. Wind Energy, 24(6), 1203-1221. https://wes.copernicus.org/articles/6/1311/2021/
References

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