Articles | Volume 6, issue 5
https://doi.org/10.5194/wes-6-1277-2021
© Author(s) 2021. 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-6-1277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Land-based wind turbines with flexible rail-transportable blades – Part 1: Conceptual design and aeroservoelastic performance
Pietro Bortolotti
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Nick Johnson
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Nikhar J. Abbas
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
Evan Anderson
Wind Energy Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA
Ernesto Camarena
Wind Energy Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA
Joshua Paquette
Wind Energy Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA
Related authors
Cory Frontin, Jeff Allen, Christopher J. Bay, Jared Thomas, Ethan Young, and Pietro Bortolotti
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-103, https://doi.org/10.5194/wes-2025-103, 2025
Preprint under review for WES
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Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
Pietro Bortolotti, Lee Jay Fingersh, Nicholas Hamilton, Arlinda Huskey, Chris Ivanov, Mark Iverson, Jonathan Keller, Scott Lambert, Jason Roadman, Derek Slaughter, Syhoune Thao, and Consuelo Wells
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Revised manuscript accepted for WES
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This study compares a wind turbine with blades behind the tower (downwind) to the traditional upwind design. Testing a 1.5 MW turbine at NREL’s Flatirons Campus, we measured performance, loads, and noise. Numerical models matched well with observations. The downwind setup showed higher fatigue loads and sound variations but also an unexpected power improvement. Downwind rotors might be a valid alternative for future floating offshore wind applications.
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
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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.
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022, https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
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Using highly accurate simulations within a design cycle is prohibitively computationally expensive. We implement and present a multifidelity optimization method and showcase its efficacy using three different case studies. We examine aerodynamic blade design, turbine controls tuning, and a wind plant layout problem. In each case, the multifidelity method finds an optimal design that performs better than those obtained using simplified models but at a lower cost than high-fidelity optimization.
Ernesto Camarena, Evan Anderson, Josh Paquette, Pietro Bortolotti, Roland Feil, and Nick Johnson
Wind Energ. Sci., 7, 19–35, https://doi.org/10.5194/wes-7-19-2022, https://doi.org/10.5194/wes-7-19-2022, 2022
Short summary
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The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway to further increasing the size of land-based wind turbines.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
Short summary
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The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Cory Frontin, Jeff Allen, Christopher J. Bay, Jared Thomas, Ethan Young, and Pietro Bortolotti
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-103, https://doi.org/10.5194/wes-2025-103, 2025
Preprint under review for WES
Short summary
Short summary
Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
Pietro Bortolotti, Lee Jay Fingersh, Nicholas Hamilton, Arlinda Huskey, Chris Ivanov, Mark Iverson, Jonathan Keller, Scott Lambert, Jason Roadman, Derek Slaughter, Syhoune Thao, and Consuelo Wells
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-8, https://doi.org/10.5194/wes-2025-8, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study compares a wind turbine with blades behind the tower (downwind) to the traditional upwind design. Testing a 1.5 MW turbine at NREL’s Flatirons Campus, we measured performance, loads, and noise. Numerical models matched well with observations. The downwind setup showed higher fatigue loads and sound variations but also an unexpected power improvement. Downwind rotors might be a valid alternative for future floating offshore wind applications.
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.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Kelsey Shaler, Benjamin Anderson, Luis A. Martínez-Tossas, Emmanuel Branlard, and Nick Johnson
Wind Energ. Sci., 8, 383–399, https://doi.org/10.5194/wes-8-383-2023, https://doi.org/10.5194/wes-8-383-2023, 2023
Short summary
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Free-vortex wake (OLAF) and low-fidelity blade-element momentum (BEM) structural results are compared to high-fidelity simulation results for a flexible downwind turbine for varying inflow conditions. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions. Differences between OLAF and BEM results were dominated by yaw misalignment angle, with varying shear exponent and turbulence intensity causing more subtle differences.
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022, https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Short summary
Using highly accurate simulations within a design cycle is prohibitively computationally expensive. We implement and present a multifidelity optimization method and showcase its efficacy using three different case studies. We examine aerodynamic blade design, turbine controls tuning, and a wind plant layout problem. In each case, the multifidelity method finds an optimal design that performs better than those obtained using simplified models but at a lower cost than high-fidelity optimization.
Ernesto Camarena, Evan Anderson, Josh Paquette, Pietro Bortolotti, Roland Feil, and Nick Johnson
Wind Energ. Sci., 7, 19–35, https://doi.org/10.5194/wes-7-19-2022, https://doi.org/10.5194/wes-7-19-2022, 2022
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway to further increasing the size of land-based wind turbines.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
Short summary
Short summary
The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Cited articles
Abbas, N., Zalkind, D., Pao, L., and Wright, A.: A Reference Open-Source Controller for Fixed and Floating Offshore Wind Turbines, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-19, in review, 2021. a
Abbas, N. J., Wright, A., and Pao, L.: An Update to the National Renewable
Energy Laboratory Baseline Wind Turbine Controller, J. Phys.
Conf. Ser. 1452, 012002, https://doi.org/10.1088/1742-6596/1452/1/012002,
2020. a
Bertagnolio, F., Madsen, H. A., and Fischer, A.: Analysis of low-frequency
noise from wind turbines using a temporal noise code, Proceedings of the 23rd
International Congress on Acoustics, Aachen, Germany, 9 Sep 2019–13 September 2019, 4414–4421, 2019. a
Bir, G. S.: User’s Guide to PreComp (Pre-Processor for Computing Composite
Blade Properties), Tech. rep., National Renewable Energy Laboratory, https://doi.org/10.2172/876556, 2006. a, b
Bolinger, M., Lantz, E., Wiser, R., Hoen, B., Rand, J., and Hammond, R.:
Opportunities for and challenges to further reductions in the “specific
power” rating of wind turbines installed in the United States, Wind
Engineering, 45, 351–368, https://doi.org/10.1177/0309524X19901012, 2020. a
Bortolotti, P., Berry, D., Murray, R., Gaertner, E., Jenne, D., Damiani, R.,
Barter, G., and Dykes, K.: A Detailed Wind Turbine Blade Cost Model, Tech.
rep., National Renewable Energy Laboratory,
https://doi.org/10.2172/1529217,
2019a. a, b
Bortolotti, P., Bottasso, C. L., Croce, A., and Sartori, L.: Integration of
multiple passive load mitigation technologies by automated design
optimization—The case study of a medium-size onshore wind turbine, Wind
Energy, 22, 65–79, https://doi.org/10.1002/we.2270, 2019b. a, b, c
Bortolotti, P., Tarres, H. C., Dykes, K., Merz, K., Sethuraman, L., Verelst,
D., and Zahle, F.: IEA Wind Task 37 on Systems Engineering in Wind Energy –
WP2.1 Reference Wind Turbines, Tech. rep., International Energy Agency,
https://doi.org/10.2172/1529216,
2019c. a, b
Camarena, E., Anderson, E., Paquette, J., Bortolotti, P., Feil, R., and Johnson, N.: Land-based wind turbines with flexible rail transportable blades – Part II: 3D FEM design optimization of the rotor blades, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-74, in review, 2021. a, b, c, d, e, f, g
Carron, W. S. and Bortolotti, P.: Innovative rail transport of a supersized
land-based wind turbine blade, J. Phys. Conf. Ser., 1618, 042041,
https://doi.org/10.1088/1742-6596/1618/4/042041, 2020. a, b
Chen, H., Yu, W., and Capellaro, M.: A critical assessment of computer tools
for calculating composite wind turbine blade properties, Wind Energy, 13,
497–516, https://doi.org/10.1002/we.372, 2010. a
Dykes, K., Damiani, R., Roberts, O., and Lantz, E.: Analysis of Ideal Towers
for Tall Wind Applications, 2018 Wind Energy Symposium, 8–12 January 2018, Kissimmee, Florida, https://doi.org/10.2514/6.2018-0999, 2018. a
Ennis, B. L., Kelley, C. L., Naughton, B. T., Norris, B., Das, S., Lee, D., and
Miller, D.: Optimized Carbon Fiber Composites in Wind Turbine Blade Design,
Tech. rep., Sandia National Laboratories, https://doi.org/10.2172/1592956, 2019. a, b
Feil, R., Pflumm, T., Bortolotti, P., and Morandini, M.: A cross-sectional
aeroelastic analysis and structural optimization tool for slender composite
structures, Compos. Struct., 253, 112755,
https://doi.org/10.1016/j.compstruct.2020.112755, 2020. a, b
Fingersh, L. J., Hand, M., and Laxson, A.: Wind Turbine Design Cost and
Scaling Model, Tech. rep., National Renewable Energy Laboratory,
https://doi.org/10.2172/897434, 2006. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G.,
Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R.,
Bredmose, H., Dykes, K., Sheilds, M., Allen, C., and Viselli, A.: Definition
of the IEA 15 MW Offshore Reference Wind Turbine, Tech. rep., International
Energy Agency, https://doi.org/10.2172/1603478,
2020. a, b
Gray, J. S., Hwang, J. T., Martins, J. R. R. A., Moore, K. T., and Naylor,
B. A.: OpenMDAO: An open-source framework for multidisciplinary design,
analysis, and optimization, Struct. Multidiscip. O.,
59, 1075–1104, https://doi.org/10.1007/s00158-019-02211-z, 2019. a
Griffin, D. A., Starnes, S., Smith, K. J., and McCoy, T. J.: R&D Pathways for
Supersized Wind Turbine Blades, Tech. rep., Lawrence Berkeley National
Laboratory,
https://doi.org/10.2172/1498695,
2019. a, b
Guo, Y., Parsons, T., King, R., Dykes, K., and Veers, P.: An Analytical
Formulation for Sizing and Estimating the Dimensions and Weight of Wind
Turbine Hub and Drivetrain Components, Tech. rep., National Renewable Energy
Laboratory, https://doi.org/10.2172/1215033,
2015. a
IEA Wind Task 37: WindIO, v1.0, GitHub [data set], available at: https://github.com/IEAWindTask37/windIO, last access: 20 September 2021. a
Jasa, J., Bortolotti, P., Zalkind, D., and Barter, G.: Effectively using multifidelity optimization for wind turbine design, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-56, in review, 2021.
a
Johnson, N., Paquette, J., Bortolotti, P., Ennis, B., Bolinger, M., Camarena,
E., and Anderson, E.: BAR Phase I Final Report, Tech. rep., National
Renewable Energy Laboratory, 2021. a
Mandell, J. F., Samborsky, D. D., Miller, D. A., Agastra, P., and Sears, A. T.:
Analysis of SNL/MSU/DOE Fatigue Database Trends for Wind Turbine Blade
Materials, 2010–2015, Tech. rep., Sandia National Laboratories,
https://doi.org/10.2172/1431256, 2016. a
Moriarty, P. J. and Hansen, A.: AeroDyn Theory Manual, Tech. rep., National
Renewable Energy Laboratory, https://doi.org/10.2172/15014831, 2005. a
Ning, A. and Petch, D.: Integrated design of downwind land-based wind turbines
using analytic gradients, Wind Energy, 19, 2137–2152, https://doi.org/10.1002/we.1972,
2016. a, b, c
Ning, S. A.: A simple solution method for the blade element momentum equations
with guaranteed convergence, Wind Energy, 17, 1327–1345,
https://doi.org/10.1002/we.1636, 2014. a, b
NREL: WEIS, v0.1, GitHub [code] available at: https://github.com/WISDEM/WEIS (last access: 8 April 2021), 2021c. a
NREL: BAR Designs, v1.0, GitHub [data set], available at: https://github.com/NREL/BAR_Designs, last access: 20 September 2021d. a
Resor, B., Paquette, J., Laird, D., and Griffith, D. T.: An Evaluation of Wind
Turbine Blade Cross Section Analysis Techniques, AIAA 2010-2575,
https://doi.org/10.2514/6.2010-2575, 2010. a
Shaler, K., Branlard, E., and Platt, A.: OLAF User’s Guide and Theory
Manual, Tech. rep., National Renewable Energy Laboratory,
https://doi.org/10.2172/1659853, 2020. a
Stehly, T. and Beiter, P.: 2018 Cost of Wind Energy Review, Tech. rep.,
National Renewable Energy Laboratory, https://doi.org/10.2172/1581952, 2019. a, b, c, d
TUM: SONATA, v0.4.0, GitHub [code], available at: https://gitlab.lrz.de/HTMWTUM/SONATA, last access: 8 April 2021. a
Wang, Q., Sprague, M. A., Jonkman, J., Johnson, N., and Jonkman, B.: BeamDyn: a
high-fidelity wind turbine blade solver in the FAST modular framework, Wind
Energy, 20, 1439–1462, https://doi.org/10.1002/we.2101, 2017. a
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway for further increasing the size of land-based wind turbines.
The length of rotor blades of land-based wind turbines is currently constrained by logistics....
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