Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1731-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1731-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Flutter behavior of highly flexible blades for two- and three-bladed wind turbines
Mayank Chetan
UTD Center for Wind Energy, Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, Texas, USA
Shulong Yao
UTD Center for Wind Energy, Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, Texas, USA
UTD Center for Wind Energy, Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, Texas, USA
Related authors
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Daniel S. Zalkind, Gavin K. Ananda, Mayank Chetan, Dana P. Martin, Christopher J. Bay, Kathryn E. Johnson, Eric Loth, D. Todd Griffith, Michael S. Selig, and Lucy Y. Pao
Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, https://doi.org/10.5194/wes-4-595-2019, 2019
Short summary
Short summary
We present a model that both (1) reduces the computational effort involved in analyzing design trade-offs and (2) provides a qualitative understanding of the root cause of fatigue and extreme structural loads for wind turbine components from the blades to the tower base. We use this model in conjunction with design loads from high-fidelity simulations to analyze and compare the trade-offs between power capture and structural loading for large rotor concepts.
Md Sanower Hossain and D. Todd Griffith
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-156, https://doi.org/10.5194/wes-2024-156, 2024
Preprint under review for WES
Short summary
Short summary
The document presents an experimental study on the parked loads of floating vertical axis wind turbines (VAWTs) in a wind and waves basin, focusing on the effects of wind speed, solidity, and floating platform dynamics. Findings show that higher wind speed, and higher solidity generally increase the parked loads, while a floating platform introduces additional effects due to tilting. A semi-numerical model was also presented to predict the parked loads, which helps enhance VAWT design.
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Mohammad Sadman Sakib and D. Todd Griffith
Wind Energ. Sci., 7, 677–696, https://doi.org/10.5194/wes-7-677-2022, https://doi.org/10.5194/wes-7-677-2022, 2022
Short summary
Short summary
This paper presents a comprehensive aerodynamic design study for a 5 MW Darrieus offshore VAWT in the context of multi-megawatt floating VAWTs. This study systematically analyzes the effect of different, important design variables including the number of blades, aspect ratio and blade chord tapering in a comprehensive load analysis of both the parked and operating aerodynamic loads including turbine power performance analysis using a vortex-based aerodynamic model.
Daniel S. Zalkind, Gavin K. Ananda, Mayank Chetan, Dana P. Martin, Christopher J. Bay, Kathryn E. Johnson, Eric Loth, D. Todd Griffith, Michael S. Selig, and Lucy Y. Pao
Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, https://doi.org/10.5194/wes-4-595-2019, 2019
Short summary
Short summary
We present a model that both (1) reduces the computational effort involved in analyzing design trade-offs and (2) provides a qualitative understanding of the root cause of fatigue and extreme structural loads for wind turbine components from the blades to the tower base. We use this model in conjunction with design loads from high-fidelity simulations to analyze and compare the trade-offs between power capture and structural loading for large rotor concepts.
Related subject area
Thematic area: Wind technologies | Topic: Systems engineering
Aerodynamic effects of leading-edge erosion in wind farm flow modeling
Control co-design optimization of floating offshore wind turbines with tuned liquid multi-column dampers
Designing wind turbines for profitability in the day-ahead markets
Knowledge engineering for wind energy
HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries
Drivers for optimum sizing of wind turbines for offshore wind farms
The eco-conscious wind turbine: design beyond purely economic metrics
A comparison of eight optimization methods applied to a wind farm layout optimization problem
Optimization of wind farm operation with a noise constraint
Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 1811–1826, https://doi.org/10.5194/wes-9-1811-2024, https://doi.org/10.5194/wes-9-1811-2024, 2024
Short summary
Short summary
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Wei Yu, Sheng Tao Zhou, Frank Lemmer, and Po Wen Cheng
Wind Energ. Sci., 9, 1053–1068, https://doi.org/10.5194/wes-9-1053-2024, https://doi.org/10.5194/wes-9-1053-2024, 2024
Short summary
Short summary
Integrating a tuned liquid multi-column damping (TLMCD) into a floating offshore wind turbine (FOWT) is challenging. The synergy between the TLMCD, the turbine controller, and substructure dynamics affects the FOWT's performance and cost. A control co-design optimization framework is developed to optimize the substructure, the TLMCD, and the blade pitch controller simultaneously. The results show that the optimization can significantly enhance FOWT system performance.
Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-43, https://doi.org/10.5194/wes-2024-43, 2024
Revised manuscript accepted for WES
Short summary
Short summary
In a subsidy-free era, there is a need to optimize turbines to maximize the revenue of the farm instead of minimizing the LCoE. A wind farm-level modeling framework with a simplified market model to optimize the size of wind turbines to maximize revenue-based metrics like IRR/NPV. The results show that the optimum turbine size is driven mainly by the choice of the economic metric and the market price scenario, with an LCoE-optimized design already performing well w.r.t. metrics like IRR.
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
Wind Energ. Sci., 9, 883–917, https://doi.org/10.5194/wes-9-883-2024, https://doi.org/10.5194/wes-9-883-2024, 2024
Short summary
Short summary
This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.
Juan Pablo Murcia Leon, Hajar Habbou, Mikkel Friis-Møller, Megha Gupta, Rujie Zhu, and Kaushik Das
Wind Energ. Sci., 9, 759–776, https://doi.org/10.5194/wes-9-759-2024, https://doi.org/10.5194/wes-9-759-2024, 2024
Short summary
Short summary
A methodology for an early design of hybrid power plants (wind, solar, PV, and Li-ion battery storage) consisting of a nested optimization that sizes the components and internal operation optimization. Traditional designs that minimize the levelized cost of energy give worse business cases and do not include storage. Optimal operation balances the increasing revenues and faster battery degradation. Battery degradation and replacement costs are needed to estimate the viability of hybrid projects.
Mihir Mehta, Michiel Zaaijer, and Dominic von Terzi
Wind Energ. Sci., 9, 141–163, https://doi.org/10.5194/wes-9-141-2024, https://doi.org/10.5194/wes-9-141-2024, 2024
Short summary
Short summary
Turbines are becoming larger. However, it is important to understand the key drivers of turbine design and explore the possibility of a global optimum, beyond which further upscaling might not reduce the cost of energy. This study explores, for a typical farm, the entire turbine design space with respect to rated power and rotor diameter. The results show a global optimum that is subject to various modeling uncertainties, farm design conditions, and policies with respect to wind farm tendering.
Helena Canet, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci., 8, 1029–1047, https://doi.org/10.5194/wes-8-1029-2023, https://doi.org/10.5194/wes-8-1029-2023, 2023
Short summary
Short summary
We propose a new approach to design that aims at optimal trade-offs between economic and environmental goals. New environmental metrics are defined, which quantify impacts in terms of CO2-equivalent emissions produced by the turbine over its entire life cycle. For some typical onshore installations in Germany, results indicate that a 1 % increase in the cost of energy can buy about a 5 % decrease in environmental impacts: a small loss for the individual can lead to larger gains for society.
Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning
Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, https://doi.org/10.5194/wes-8-865-2023, 2023
Short summary
Short summary
This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
Camilla Marie Nyborg, Andreas Fischer, Pierre-Elouan Réthoré, and Ju Feng
Wind Energ. Sci., 8, 255–276, https://doi.org/10.5194/wes-8-255-2023, https://doi.org/10.5194/wes-8-255-2023, 2023
Short summary
Short summary
Our article presents a way of optimizing the wind farm operation by keeping the emitted noise level below a defined limit while maximizing the power output. This is done by switching between noise reducing operational modes. The method has been developed by using two different noise models, one more advanced than the other, to study the advantages of each model. Furthermore, the optimization method is applied to different wind farm cases.
Cited articles
Abdel Hafeez, M. M. and El-Badawy, A. A.: Flutter Limit Investigation for a
Horizontal Axis Wind Turbine Blade, J. Vibrat. Acoust., 140, 041014, https://doi.org/10.1115/1.4039402, 2018. a
Bay, C. J., Damiani, R., Fingersh, L. J., Hughes, S., Chetan, M., Yao, S.,
Griffith, D. T., Ananda, G. K., Selig, M. S., Zalkind, D., Pao, L., Martin,
D., Johnson, K., Kaminski, M., and Loth, E.: Design and Testing of a Scaled Demonstrator Turbine at the National Wind Technology Center, in: AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics, San Diego, California, https://doi.org/10.2514/6.2019-1068, 2019. a
Berg, J. C. and Resor, B. R.: Numerical manufacturing and design tool (NuMAD V2.0) for wind turbine blades: User's guide, Technical Report No. SAND2012-728, Sandia National Laboratories, Albuquerque, NM, https://doi.org/10.2172/1051715, 2012. a
Bergami, L.: Aeroservoelastic stability of a 2D airfoil section equipped with a trailing edge flap, Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi, https://orbit.dtu.dk/en/publications/aeroelastic-stability-of-a-2d-airfoil-section-equipped-with-a-tra
(last access: August 2022), 2008. a
Bir, G. S.: User's Guide to PreComp (Pre-Processor for Computing Composite Blade Properties), Tech. Rep. NREL/TP-500-38929, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/876556, 2006. a
Bortolotti, P., Tarres, H. C., Dykes, K. L., Merz, K., Sethuraman, L., Verelst, D., and Zahle, F.: IEA Wind TCP Task 37: Systems Engineering in
Wind Energy – WP2.1 Reference Wind Turbines, Tech. Rep. NREL/TP-5000-73492, 1529216, IEA, https://doi.org/10.2172/1529216, 2019. a
Bossanyi, E., Wright, A., and Fleming, P.: Controller field tests on the NREL
CART2 turbine, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/1001440, 2010. a
Chen, Y. and Griffith, D. T.: Mode Shape Recognition of Complicated Spatial Beam-Type Structures via Polynomial Shape Function Correlation, Exp. Tech.,
https://doi.org/10.1007/s40799-021-00505-w, 2021. a
Chetan, M., Griffith, D. T., and Yao, S.: Flutter Predictions in the Design of Extreme-Scale Segmented Ultralight Morphing Rotor Blades, in: AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics, San Diego, California, https://doi.org/10.2514/6.2019-1298, 2019a. a, b
Chetan, M., Sakib, M. S., Griffith, D. T., and Yao, S.: AeroStructural
Design Study of Extreme-Scale Segmented Ultralight Morphing Rotor Blades, in: AIAA Aviation 2019 Forum, American Institute of Aeronautics and Astronautics, Dallas, Texas, https://doi.org/10.2514/6.2019-3347, 2019b. a
Farsadi, T. and Kayran, A.: Classical flutter analysis of composite wind
turbine blades including compressibility, Wind Energy, 24, 69–91,
https://doi.org/10.1002/we.2559, 2021. a
Fingersh, L. J. and Johnson, K.: Controls Advanced Research Turbine (CART) Commissioning and Baseline Data Collection, Tech. Rep. NREL/TP-500-32879, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/15002211, 2002. a
Griffith, D. and Richards, P. W.: The SNL100-03 Blade: Design Studies with Flatback Airfoils for the Sandia 100-meter Blade, Tech. Rep. SAND2014-18129, SNL-NM – Sandia National Lab., Albuquerque, NM, USA, https://doi.org/10.2172/1159116, 2014. a, b, c
Griffith, D. T.: The SNL100-01 blade: carbon design studies for the Sandia
100-meter blade, Sandia National Laboratories Technical Report SAND2013-1178, SNL-NM – Sandia National Lab., https://doi.org/10.2172/1093695, 2013a. a, b
Griffith, D. T.: The SNL100-02 blade: advanced core material design studies
for the Sandia 100-meter blade, Sandia National Laboratories Technical
Report SAND2013-10162, SNL-NM – Sandia National Lab., https://doi.org/10.2172/1147201, 2013b. a, b
Griffith, D. T. and Ashwill, T. D.: The Sandia 100-meter all-glass baseline
wind turbine blade: SNL100-00, Sandia National Laboratories Technical
Report SAND2011-3779, SNL-NM – Sandia National Lab., https://energy.sandia.gov/wp-content/gallery/uploads/SAND2011-3779.pdf (last access: August 2022), 2011. a
Griffith, D. T. and Chetan, M.: Assessment of flutter prediction and trends in the design of large-scale wind turbine rotor blades, J. Phys.: Conf. Ser., 1037, 042008, https://doi.org/10.1088/1742-6596/1037/4/042008, 2018. a, b, c, d
Griffith, D. T. and Resor, B. R.: Description of model data for SNL13. 2-00-Land: A 13.2 MW land-based turbine model with SNL100-00 Blades, Sandia National Laboratories Technical Report SAND2011-9310P, SNL-NM – Sandia National Lab., https://energy.sandia.gov/wp-content/gallery/uploads/dlm_uploads/SNL13.2-00-Land-Turbine-Model-Description_v0.pdf
(last access: August 2022), 2011. a
Hansen, M.: Stability Analysis of Three-Bladed Turbines Using an Eigenvalue Approach, in: 42nd AIAA Aerospace Sciences Meeting and Exhibit, American Institute of Aeronautics and Astronautics, Reno, Nevada,
https://doi.org/10.2514/6.2004-505, 2004. a, b
Hansen, M. H.: Aeroelastic instability problems for wind turbines, Wind Energy, 10, 551–577, https://doi.org/10.1002/we.242, 2007. a, b, c, d
Hayman, G.: MExtemes Manual Version 1.00, Technical Report, NREL – National
Renewable Energy Laboratory, Golden, Colorado, USA, https://www.nrel.gov/wind/nwtc/assets/downloads/MCrunch_MLife_MExtremes/MExtremesManual.pdf
(last access: August 2022), 2015. a
Hayman, G. and Buhl Jr., M.: Mlife users guide for version 1.00, NREL – National Renewable Energy Laboratory, Golden, CO, 74, 112 pp., https://www.nrel.gov/wind/nwtc/assets/pdfs/mlife-user.pdf (last access: August 2022), 2012. a
Ichter, B., Steele, A., Loth, E., Moriarty, P., and Selig, M.: A morphing
downwind-aligned rotor concept based on a 13-MW wind turbine, Wind Energy, 19, 625–637, https://doi.org/10.1002/we.1855, 2016. a
Jonkman, J.: OpenFAST/openfast, original-date: 2016-08-31T20:07:10Z, https://github.com/OpenFAST/openfast (last access: August 2022), 2020. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW
Reference Wind Turbine for Offshore System Development, Tech. Rep. NREL/TP-500-38060, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/947422, 2009. a
Kallesøe, B. S. and Kragh, K. A.: Field Validation of the Stability
Limit of a Multi MW Turbine, J. Phys.: Conf. Ser., 753, 042005, https://doi.org/10.1088/1742-6596/753/4/042005, 2016. a, b
Kaminski, M.: Field Testing and Simulating Servo-Aero-Gravoelastically Scaled Rotors for Extreme-Scale Wind Turbines, PhD thesis, University of Virginia, VA, https://doi.org/10.18130/V3-MQJ2-GZ84, 2020. a
Kaminski, M., Loth, E., Zalkind, D., Pao, L., Selig, M., and Johnson, K.:
Servo-aero-gravo-elastic (SAGE) scaling and its application to a 13-MW
downwind turbine, J. Renew. Sustain. Energ., 12, 063301, https://doi.org/10.1063/5.0021171, 2020. a
Kaminski, M., Noyes, C., Loth, E., Damiani, R., Hughes, S., Bay, C., Chetan,
M., Griffith, D. T., Johnson, K., and Martin, D.: Gravo-aeroelastic scaling
of a 13-MW downwind rotor for 20 % scale blades, Wind Energy, 24, 229–245, https://doi.org/10.1002/we.2569, 2021. a
Kelley, C. L. and Paquette, J.: Investigation of flutter for large, highly
flexible wind turbine blades, J. Phys.: Conf. Ser., 1618, 052078, https://doi.org/10.1088/1742-6596/1618/5/052078, 2020. a, b, c
Lobitz, D. W.: Aeroelastic stability predictions for a MW-sized blade, Wind
Energy, 7, 211–224, https://doi.org/10.1002/we.120, 2004. a, b, c, d
Loth, E., Steele, A., Qin, C., Ichter, B., Selig, M. S., and Moriarty, P.:
Downwind pre-aligned rotors for extreme-scale wind turbines: Downwind
pre-aligned rotors for extreme-scale wind turbines, Wind Energy, 20, 1241–1259, https://doi.org/10.1002/we.2092, 2017. a
Malcolm, D. J. and Hansen, A. C.: WindPACT Turbine Rotor Design Study: June 2000–June 2002 (Revised), Tech. rep., NREL – National Renewable
Energy Lab., Golden, CO, USA, https://doi.org/10.2172/15000964, 2006. a
Martin, D. P.: Modeling, Control and Design of Extreme Scale Wind Turbines, PhD thesis, Colorado School of Mines, https://www.proquest.com/dissertations-theses/modeling-control-design-extreme-scale-wind/docview/2303231358/se-2?accountid=7120
(last access: August 2022), 2019. a
McDonnell, T. G. and Ning, A.: Reliable Mode Tracking in Gradient-Based
Optimization Frameworks with Flutter Constraints, in: AIAA Aviation 2021
Forum, virtual event, 2–6 August 2021, https://doi.org/10.2514/6.2021-3081, 2021. a
Owens, B. C., Resor, B. R., Hurtado, J. E., and Griffith, D.: Impact of
Modelinng Approach on Flutter Predictions for Very LargeWind Turbine Blade Designs, Tech. rep., SNL-NM – Sandia National Lab., Albuquerque, NM, USA, https://www-osti-gov.libproxy.utdallas.edu/biblio/1078785-impact-modeling-approach-flutter-predictions-very
(last access: August 2022), 2013. a, b, c, d, e, f
Pao, L. Y., Zalkind, D. S., Griffith, D. T., Chetan, M., Selig, M. S., Ananda, G. K., Bay, C. J., Stehly, T., and Loth, E.: Control co-design of 13 MW downwind two-bladed rotors to achieve 25 % reduction in levelized cost of wind energy, Annu. Rev. Control, 51, 331–343, https://doi.org/10.1016/j.arcontrol.2021.02.001, 2021.
a, b, c, d
Pourazarm, P., Modarres‐Sadeghi, Y., and Lackner, M.: A parametric study of
coupled-mode flutter for MW-size wind turbine blades, Wind Energy, 19, 497–514, https://doi.org/10.1002/we.1847, 2016. a, b, c, d
Qin, C. C., Loth, E., Zalkind, D. S., Pao, L. Y., Yao, S., Griffith, D. T.,
Selig, M. S., and Damiani, R.: Downwind coning concept rotor for a 25 MW
offshore wind turbine, Renew. Energy, 156, 314–327, https://doi.org/10.1016/j.renene.2020.04.039, 2020. a
Rinker, J. and Dykes, K. L.: Windpact reference wind turbines, Tech. rep.,
NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/1432194, 2018. a
Riziotis, V. A. and Madsen, H. A.: 3 – Aeroelasticity and structural dynamics of wind turbines, in: Wind Energy Systems, Woodhead Publishing Series in Energy, edited by: Sørensen, J. D. and Sørensen, J. N., Woodhead Publishing, 46–111, https://doi.org/10.1533/9780857090638.1.46, 2011. a
Volk, D. M., Kallesøe, B. S., Johnson, S., Pirrung, G. R., Riva, R., and
Barnaud, F.: Large wind turbine edge instability field validation, J Phys.: Conf. Ser., 1618, 052014, https://doi.org/10.1088/1742-6596/1618/5/052014, 2020. a, b
Wright, J. R. and Cooper, J. E.: Introduction to aircraft aeroelasticity and
loads, in: vol. 20, John Wiley & Sons, ISBN 9780470858462, 2008. a
Yao, S., Griffith, D. T., Chetan, M., Bay, C. J., Damiani, R., Kaminski, M.,
and Loth, E.: Structural Design of a Scale
Gravo-Aeroelastically Scaled Wind Turbine Demonstrator Blade for Field Testing, in: AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics, San Diego, California, https://doi.org/10.2514/6.2019-1067, 2019. a
Yao, S., Chetan, M., Griffith, D. T., Escalera Mendoza, A. S., Selig, M. S.,
Martin, D., Kianbakht, S., Johnson, K., and Loth, E.: Aero-structural design
and optimization of 50 MW wind turbine with over 250-m blades, Wind
Eng., 46, 273–295, https://doi.org/10.1177/0309524X211027355, 2021b. a, b, c
Short summary
Though large wind turbines are appealing to reduce costs, larger blades are prone to aero-elastic instabilities due to their long, slender, highly flexible nature. New rotor concepts are emerging including two-bladed rotors and downwind configurations. We introduce a comprehensive evaluation of flutter behavior including classical flutter and edgewise vibration for large-scale two-bladed rotors. The study aims to provide designers with insights to mitigate flutter in future designs.
Though large wind turbines are appealing to reduce costs, larger blades are prone to...
Altmetrics
Final-revised paper
Preprint